ClearScape Analytics™ Demonstrations via Jupyter
Welcome to ClearScape Analytics Experience. This service consists of multiple demonstrations of the industry leading in-database analytics that you can run on your own. You can modify them or use them as examples to use with your own tools against our data or small (not sensitive) data you upload. Each notebook will:
- describe the business situation,
- will attach the needed data from the cloud, and
- walk you step-by-step through the use of the ClearScape Analytics functionality.
These are functional demonstrations executed on a tiny platform with small data, but the same functionality is available on all of our platforms up to one with hundreds of nodes and petabytes of data. ClearScape Analytics allows you to apply AI, ML and advanced statistics to your data without the cost and complexity of exporting data. You can develop sophisticated models on other platforms with your favorite tools and import those models to execute in production at massive scale.
If you've never used Jupyter before, we strongly recommend reviewing the First Time User section of Getting Started.
You'll find an introduction video with tips on using this platform.
There are also tips for you if you just want to look without programming.
If you have questions or issues, click here to send an e-mail to ClearScape Analytics Support.
Table of Contents
Items in italics are coming soon.
| Getting Started | Industries | Business Function | Analytic Function | 3rd Party Tools | ||||
|---|---|---|---|---|---|---|---|---|
| First Time User | Automotive | Finance | AWS SageMaker | Python | Anomaly Detection | Beta Review | AWS | |
| I am not a programmer | Energy & Natural Resources | Marketing | Azure ML | SQL | Customer Complaint Analysis | New | ||
| Developer Information | Financial | Celebrus | Enterprise Feature Store | Parallel CPU Inferencing | Azure | |||
| Healthcare | Dataiku | Model Factory | Dashboard Notebook | |||||
| Manufacturing | H2O.ai | |||||||
| Retail | Microsoft PowerBI | |||||||
| Telco | MicroStrategy | |||||||
| Travel & Transportation | R | |||||||
| Defense | SAP Business Objects | |||||||
| SAS | ||||||||
| Tableau | ||||||||
| Vertex AI | ||||||||
| AWS Bedrock |
Getting Started
First Time User
Getting Started With Azure
Follow these steps to create your first Azure account and create an Azure Machine Learning workspace to complete our AzureML use cases.
Information
Getting Started with Azure OpenAI
Follow these instructions to setup the Azure OpenAI endpoint and generate the access Keys required to run the model.
Python Version
Introduction Video
Video description how to find demos in the index and folder view, tips on running demos and options for foreign vs local tables used in the demonstrations in your ClearScape Analytics environment.
Information
Basic Jupyter Navigation
When running a Jupyter Notebook, there are various indicators that show what is happening. This is a guide to those indicators.
SQL Version
I am not a programmer
I am not a programmer
Not everyone that uses this site will want to learn programming. Some will want to review the business cases, look at the steps for the analysis and look at the tables, charts and maps. This is a guide for those people.
Information
Developer Information
Charting and Visualizations using teradataml
The td_plot method streamlines large-scale visualizations by providing users with efficient tools to create visualizations directly within the Vantage platform
Python Version
Data Loading (Python)
Shows how to use python to load CSV data from local storage and from zipped files
Python Version
Data Loading (SQL)
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
SQL Version
Query Service REST API
Demonstration of using REST API calls to Vantage which is useful for web or mobile applications to access and maintain data.
Python Version
Using ClearScape Analytics with openAI
To ensure optimal utilization of the OpenAI API in generative AI notebooks, it is essential to establish the API keys correctly. This concise guide outlines the process of configuring OpenAI API keys for seamless integration across multiple notebooks.
Information
Convert PySpark to teradatamlspk
Convert a PySpark script to teradatamlspk syntax and generate a HTML report using Housing Prices to generate price predictions.
Python Version
tdplyr R Basics
Work through using the bgasics of the Teradata R package, tdplyr
R Version
Vantage Query Log Analysis
Analysis of sessions and queries you executed using the built-in logging facilities of Vantage.
SQL Version
Data Loading ('R')
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
R Version
Execute Custom Python Scripts in Vantage
Run an external custom python script in Vantage using Script Table Operator(STO) to execute custom python scripts on Vantage
Python Version
Tutorial on using Teradataml Widgets
Use Teradataml Widgets to display interactive prompting to generate datasets from the Vantage database.
Python Version
Introduction to Plot types using Teradataml Widgets.
This is an introduction to using the various Plot types available as widgets: Line, Bar, Mesh, Wiggle, Geometry, etc.
Python Version
Teradata Package for R Basics
Discoverer how the Teradata Package for R (tdplyr) allows users to develop and run R programs to take advantage of Big Data and Machine Learning analytic capabilities of Vantage.
Information
Initiate Parallel CPU Inferencing of Hugging Face Models in Vantage
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
SQL Basics in Jupyter
This guide will walk you through writing your first SQL queries in Jupyter. It uses some of the Vantage system tables as a source for the queries.
SQL Version
teradataml Python Basics
Introduction to Teradataml package for Python including connecting to Vantage, Teradata DataFrames, data manipulation and export to Pandas.
Python Version
Intro to Panda for Python
Provides step-by-step instructions on the basics of using Python Pandas with Jupyter notebooks.
Python Version
Charting and Visualization
Data from queries is brought to life with graphics and charts. This shows how to use the %chart magic command to display results.
SQL Version
VAL Overview
Vantage Analytics Library (VAL) is a set of over 50 functions for advanced analytics. This provides an overview and links to an 8 minute video overview.
SQL Version
Data Dictionary
This provides an index to all of the databases used by demo notebooks on this machine, allowing you to use that data for your own notebooks or BI tools.
Python Version
How to Submit Your Demos
It is very easy to submit your demo for publication. Tell us directory with the notebook and referenced files and grant us access to your database. We'll take it from there.
Python Version Video
Industries
Automotive
Anomaly Detection
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Battery Defect Analysis
Uses local data (or foreign tables on GCP) to analyze patterns of battery failure, then links to data on AWS for detailed battery measurement.
Python Version
Car Complaints
Uses geospatial techniques to locate service centers close to the complaint and outlier detection to detect part defects earlier than expected.
Python-SQL Version
Energy & Natural Resources
Carbon Footprint Analytics
A key component of ESG is Carbon Footprint. This demonstrates a part of a solution available from Teradata to integrate multiple data sources to calculate carbon footprint of various corporate activities.
Python-SQL Version
Energy Consumption Forecasting
This combines machine learning and BYOM to forecast energy consumption using Vantage to score the model at scale without having to export all data.
Python Version
Energy Consumption Forecasting Dataiku
Demonstration of using Dataiku with Vantage. Instructions provided for use with your Dataiku copy + screen shots if you don't have Dataiku. PMML model from Dataiku is imported to Vantage for execution and scoring.
Python Version
Energy Consumption Forecasting using AzureML
This leverages the power of AzureML and Teradata Vantage to enhance our machine learning capabilities and enable scalable model scoring to forecast energy consumption.
Python Version
Flood Proximity to Climate Risk Analysis
This allows enterprises to rapidly analyze this geographic-related information in real-time at any scale - effectively understanding the impacts of these climate events on entire populations.
Python Version
Financial
Anomaly Detection of Outstanding Amounts
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
Credit Card Data Preparation
Uses functions from TeradataML Python library to prepare data for analysis using data cleansing, exploration and feature engineering functions.
Python Version
Mortgage Calculator using GenAI
Use Retrieval-Augmented Generation (RAG), Lanchain and LLM models to as questions about loans and retrieve relevant data from Vantage.
Python Version
Banking Customer Churn
Uses a combination of Vantage Analytics Library to prepare data, using machine learning in python and importing the resulting PMML model into Vantage for scoring.
Python Version
Cash Demand Forecasing
Predicts the future demand for cast in Automatic Teller Machines (ATMs) using Auto Regressive Integrated Moving Average (ARIMA) using In-Database functions.
SQL Version
Consumer Complaints
Uses government consumer complaint data with SQL queries and visualizations to identify source of top complaints.
SQL Version
Credit Card Data Preparation
This shows the use of ClearScape analytics to reduce the pre-processing effort of incoming raw credit card data to prepare for analysis of potential loan defaulters.
SQL Version
Digital Identity Management
Combines ClearScape Analytics with Celebrus to track unique customers across web sessions and devices to drive personalized experiences
SQL Version
Financial Customer Journey
Uses analytic techniques to find new customers, measure marketing attribution, and maximizing marketing effectiveness
Python-SQL Version SQL Version Video
Financial Fraud Detection InDB
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version Video
Financial Fraud Detection VIA BYOM
A model that was developed externally is imported into Vantage for evaluation and execution at scale to detect fraud.
Python Version
Flood Proximity to Climate Risk Analysis
This allows enterprises to rapidly analyze this geographic-related information in real-time at any scale - effectively understanding the impacts of these climate events on entire populations.
Python Version
Insurance Policy Temporal
Show As-IS/As-Was capabilities of Vantage Temporal to dramatically simplify the SQL and improve performance for analyzing insurance policies versus claims.
SQL Version
Credit Risk Assessment using Teradataml OpenSource Functions
Use inDb functions with OpensourceML to create multiple DecisionTreeClassifiers to create multiple predictions of a Credit Risk Assessment.
Python Version
Financial Fraud Detection InDB
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
Mortgage Calculator Chatbot using GenAI: Finetune LLM
Fine-tuned an OpenAI model using RAG, LangChain and LLM models framework. Query a chatbot for answers about a mortgage and available housing within a predefiend area.
Python Version
Mortgage Calculator chatbot using GenAI: RAG
Build a conversational chatbot and ask questions about a mortgage and available housing within a predefined area using LangChain.
Python Version
Mortgage Calculator Chatbot using Trusted AI(RAG)
Experience the integration of LLM models to provide user-friendly responses to queries. RAG combines retrieval and generative approaches.
Python Version
Banking Churn Prediction with AutoML
Implement the entire lifecycle of churn prediction using BYOM, VAL and AutoML.
Python Version
Healthcare
Data Science 101 with Python
This walks through the Cross-Industry Standard Process for Data Mining (CRISP-DM) from data understanding through modeling and evaluation.
Python Version
Diabetes Prediction via BYOM H2O
This uses BYOM to import a trained H2O model to identify potential diabetes patients. BYOM allows the data scientist to create models in languages they prefer and run at scale inside Vantage without moving data.
Python-SQL Version
Diabetes Prediction via DF and GLM
Decision Forest and Generalized Linear Model are applied to identify factors that indicate diabetes. The earlier the disease is identified, the better the chance of reducing organ damage.
Python Version
Heart Failure Prediction
Machine learning is applied to the complex attributes of patients to help recognize patterns that may lead to heart failure faster than a human may recognize.
Python-SQL Version
Knee Replacement Attribution
The ClearScape Analytics Attribution function is used to determine the weight of various events that precede the final outcome, in this example, knee replacement.
SQL Version
Knee Replacement nPath
This uses the ClearScape Analytics nPath® function to provide visuals on the events leading up to the final outcome, in this case, knee replacement.
Python Version
Cancer Prediction using the TDAPIClient and VertexAI
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Mental Health Chat with Fine-tuned OpenAI Model
Train a GPT-3.5 Turbo model using the OpenAI API endpoint. Ask mental health questions using a chat input box.
Python Version
Parkinson's Disease Prediction using Signal Processing
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
Predicting Medical Expenses in Healthcare
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
Cancer Prediction using Teradata and the SageMaker API
Use the Vantage SageMaker API feature to connect to an Amazon Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Manufacturing
Anomaly Detection
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Battery Defect Analysis
Uses local data (or foreign tables on GCP) to analyze patterns of battery failure, then links to data on AWS for detailed battery measurement.
Python Version
Car Complaints
Uses geospatial techniques to locate service centers close to the complaint and outlier detection to detect part defects earlier than expected.
Python-SQL Version
Green Manufacturing
Uses analytic and ML techniques to predict how long vehicle testing will take based on combination of features installed.
Python Version
Predictive Maintenance
Uses ML functions to predict failures to identify and mitigate potential machine failures before they occur.
Python-SQL Version
Remaining Useful Life Forecasting
Applies machine learning to predict Remaining Useful Life (RUL) of jet engines, allowing scheduling of maintenance and replacement before failure occurs and reduces the cost of maintenance and replacement.
Python-SQL Version
Anomaly Detection in Spot Welding Process - Trusted AI
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python Version
Retail
Product Recommentations via TDApiClient
Build a product recommendation system using OpenAI embeddings and Vantage in db_function VectorDistance. We will also use Vantage as VectorDB, to store the embeddings.
Python Version
Broken Digital Journey
This uses nPath® analysis to show the retail events that prevent the ultimate objective: a purchase. This uses interactive Sankey diagrams to understand the problems.
Python Version Video
Competitor Proximity Geospatial Analysis
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
Customer Behavior Analysis
Analysis of customer purchase behavior using nPath® analysis in Python with visualization using Sankey diagrams.
Python Version SQL Version
Deep History via Object Store
This demonstrates integration of local data or foreign tables on GCP and integration across cloud providers to detailed historical sales records on AWS.
SQL Version
Hyper-Personalization
Hyper-personalization creates models from customer iteractions on multiple channels to determine the "Next Best Offer" for the individual.
Python-SQL Version
K-Means Clustering and ML model
This uses the unsupervised K-Means ML algorithm to identify patterns in retail purchases.
SQL Version
Marketing Campaign Effectiveness
Examines the results of campaigns by various customer attributes then uses correlation, outlier elimination, and machine learning to identify the best campaigns.
Python-SQL Version
Multi-Touch Attribution for Business Analyst
This version of the Multi-Touch Attribution demonstration is focused on the interests of the Business Analyst.
Python-SQL Version
Multi-Touch Attribution for Data Scientist
Demonstrates attribution of customer behavior via single touch and multi-touch rule-based models using statistical and algorithmic models. Multiple approaches are demonstrated.
Python-SQL Version
Product Recommendations via Open Source
Uses FlagEmbedding from HuggingFace plus Vantage in-database functions to assess the vector distance between the product entered and similar products.
Python Version
Product Recommendations via OpenAI
Uses OpenAIEmbeddings and Vantage in-database function to assists consumers in receiving product recommendations
Python Version
Retail Demand Forecasting
This creates an ARIMA time series model based on holidays and merchandising activities impacting store sales for a hypermarket retailer.
Python Version
Sales Forecasting using UAF
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
Store Sales Forecast via ARIMA
Forecasts total store sales using ARIMA (AutoRegressive Integrated Moving Average)
Python Version
Store Sales Forecast via Prophet
Uses the Script Table Operator (STO) to run the H20 machine learning library as an extension to ClearScape Analytics. H20 runs in parallel inside the Vantage database without exporting data to another platform.
Python-SQL Version
Text Term Frequency
Use NGram splitter to analyze comments retail products to determine patterns of words used to describe products.
Python Version Python-SQL Version SQL Version
Customer Reviews Analysis using GenAI
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
Product Recommendation via AWS Bedrock
Use AWS Bedrock, Embedding from HuggingFace and Vantage in-DB functions to assist in providing product recommendations to develop a recipe assistant chatbot.
Python Version
Natural Language Processing
Use NLP for Sentiment Analysis, perform Kmeans clustering, execute Principal Component Analysis (PCA) using comments received by a Retail Store
Python-SQL Version
Enhance Product Hierarchies with In-DB K-means Clustering
Enhance a product hierarchy by leveraging time series analysis and clustering techniques
Python Version
Generate Teradata SQL with GenAI and AWS Bedrock
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
Sales Forecasting using Teradata AUTOARIMA
Forecast the amount of future sales by developing a time-series modelling pipeline using Vantage and In-DB analytics
Python Version
Telco
Telco 5G Quality of Experience
Quality of service analysis of 5G vs 4G video for different situations such as Indoor, outdoor, and moble.
SQL Version
Graph Analysis of CDR Records
Uses graph analysis to identify communities and key influencers within Call Data Records. This uses Script Table Operator to invoke external procedures to work inside the Vantage database without exporting data.
Python-SQL Version
Telco Customer Churn
This uses logistic regression for supervised learning to predict the probability of a customer switching vendors based on usage patterns, billing information demographics and interactions. XGBoost is then used in database to improve the prediction.
Python Version Video
Telco Network Coverage
Demonstrates the ability of Geospatial to show signal strength, coverage areas and travel path of customers through cell tower coverage area.
Python Version
Travel & Transportation
Chatbot using GenAI with insurance PDF document
Uses TD_VectorDistance in Vantage to create a chatbot using Retrieval-Augmented Generation (RAG), Langchain, and LLM to answer questions using a 24 page travelers insurance policy PDF as source data to answer questions about coverage.
Python Version Python-SQL Version
Shipping Time Prediction
Use ClearScape Analytics to develop a robust system that can reliably estimate delivery dates, accounting for handling time, transit time, and other relevant variables affecting the actual delivery timeframe.
Python-SQL Version
Supply Chain Visibility
Shows the integration of data from warehouse, trucking company, and customer Order Management Systems (OMS) using temporal to reconcile different update frequencies and show rerouting when warehouse is unavailable.
Python Version
Vehicle Routing for Delivery
Demonstrates advanced analytics to find lowest cost routes to deliver a large volume of packages by a fleet of vehicles.
Python-SQL Version
4D Analytics on bike sharing
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Air Passenger Forecasting
Applies Auto Regressive Integrated Moving Average (ARIMA) analysis to forecast airplane passenger volume.
SQL Version
NYC Taxi Geospatial
Applies geospatial functions to analysis of NYC Taxi pickups and drop off locations.
Python-SQL Version
Remaining Useful Life Forecasting
Applies machine learning to predict Remaining Useful Life (RUL) of jet engines, allowing scheduling of maintenance and replacement before failure occurs and reduces the cost of maintenance and replacement.
Python-SQL Version
Train Delay Path Analysis
Uses nPath displays to show relationship of delays and predictive models to anticipate potential delays and enable proactive planning.
Python Version
Defense
Signal Processing and Classification
Use the Unbounded Array Framework in ClearScape Analytics to classify sonar signals. Extract and engineer features to use in training and scoring our models.
Python Version
Business Function
Marketing
Competitor Proximity Geospatial Analysis
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
Customer 360
This integrates data about customers from multiple sources using multiple matching techniquest to create the "Golden Customer Record" and calculate LTV.
SQL Version
Digital Identity Management
Combines ClearScape Analytics with Celebrus to track unique customers across web sessions and devices to drive personalized experiences
SQL Version
Generative AI Question Answering
Uses OpenAI to translate english language questions into SQL queries to run against a table of marketing data. This includes a link to a notebook with instructions for getting the required OpenAI API key.
Python Version Video
Hyper-Personalization
Hyper-personalization creates models from customer iteractions on multiple channels to determine the "Next Best Offer" for the individual.
Python-SQL Version
Marketing Campaign Effectiveness
Examines the results of campaigns by various customer attributes then uses correlation, outlier elimination, and machine learning to identify the best campaigns.
Python-SQL Version
Multi-Touch Attribution for Business Analyst
This version of the Multi-Touch Attribution demonstration is focused on the interests of the Business Analyst.
Python-SQL Version
Multi-Touch Attribution for Data Scientist
Demonstrates attribution of customer behavior via single touch and multi-touch rule-based models using statistical and algorithmic models. Multiple approaches are demonstrated.
Python-SQL Version
Store Sales Forecast via ARIMA
Forecasts total store sales using ARIMA (AutoRegressive Integrated Moving Average)
Python Version
Analytic Function
Arima
Sales Forecasting using Teradata AUTOARIMA
Forecast the amount of future sales by developing a time-series modelling pipeline using Vantage and In-DB analytics
Python Version
Aspect-Based Sentiment Analysis
Insights from Sentiment Analysis with AWS Bedrock
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
Python Version
AutoML
Banking Churn Prediction with AutoML
Implement the entire lifecycle of churn prediction using BYOM, VAL and AutoML.
Python Version
Complaint Summarization
Complaints Classification with AWS Bedrock
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Summarization with AWS Bedrock
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Complaints Classification
Complaints Classification with Google Gemini
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaint Summarization
Complaints Summarization with Google Gemini
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Complaints Classification
Complaints Classification with OpenAI
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaint Summarization
Complaints Summarization with OpenAI
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Complaints Clustering
Complaints Clustering with AWS Bedrock
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Semantic Clustering using OpenSource Language Models
Bring a language model into Vantage and run NLP models in the database
Python Version
Complaints Clustering with Google Gemini
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Clustering with OpenAI
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Customer 360
Customer Complaints Analysis with Customer360 with AWS Bedrock
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Customer Complaints Analysis with Customer360 with OpenAI
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Data Preparation
Credit Card Data Preparation
Uses functions from TeradataML Python library to prepare data for analysis using data cleansing, exploration and feature engineering functions.
Python Version SQL Version
Data Prep and Transformation
This demonstrates a subset of the over 100 analytic functions in the teradataml package for Python
Python Version Python-SQL Version
Outlier Analysis
Outliers in an analysis skew the results and make it difficult to recognize the main patterns. ClearScape Analytics has tools to remove outliers automatically.
SQL Version
Sales Forecasting using UAF
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
Data Loading ('R')
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
R Version
Predicting Medical Expenses in Healthcare
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
Descriptive Statistics
Sales Forecasting using UAF
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
VAL Descriptive Statistics
This performs in-database analysis of data values, distribution, histograms, and text field analysis using SQL to access the Vantage Analytics Library.
SQL Version
VAL teradataml Demo
Demonstrated the use of Teradataml in Python to perform descriptive statistics, transformation, model building model evaluation and scoring.
Python Version
Emotion Detection
Insights from Sentiment Analysis with AWS Bedrock
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Fraud Detection
Financial Fraud Detection InDB
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
Generative AI
Chatbot using GenAI with insurance PDF document
Uses TD_VectorDistance in Vantage to create a chatbot using Retrieval-Augmented Generation (RAG), Langchain, and LLM to answer questions using a 24 page travelers insurance policy PDF as source data to answer questions about coverage.
Python Version Python-SQL Version
Mortgage Calculator using GenAI
Use Retrieval-Augmented Generation (RAG), Lanchain and LLM models to as questions about loans and retrieve relevant data from Vantage.
Python Version
Product Recommentations via TDApiClient
Build a product recommendation system using OpenAI embeddings and Vantage in db_function VectorDistance. We will also use Vantage as VectorDB, to store the embeddings.
Python Version
Generative AI Question Answering
Uses OpenAI to translate english language questions into SQL queries to run against a table of marketing data. This includes a link to a notebook with instructions for getting the required OpenAI API key.
Python Version Video
Product Recommendations via Open Source
Uses FlagEmbedding from HuggingFace plus Vantage in-database functions to assess the vector distance between the product entered and similar products.
Python Version
Product Recommendations via OpenAI
Uses OpenAIEmbeddings and Vantage in-database function to assists consumers in receiving product recommendations
Python Version
Complaints Classification with OpenAI
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with OpenAI
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with OpenAI
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with OpenAI
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Generate Teradata SQL with GenAI and AWS Bedrock
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
Sentiment Analysis with OpenAI
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with OpenAI
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with OpenAI
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer Reviews Analysis using GenAI
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
Grocery Recommendations using GenAI
Use context-based product recommendations powered by GenAI Large Language Models to enhance a shopping experience.
Python-SQL Version
Mortgage Calculator Chatbot using Trusted AI(RAG)
Experience the integration of LLM models to provide user-friendly responses to queries. RAG combines retrieval and generative approaches.
Python Version
Product Recommendation via AWS Bedrock
Use AWS Bedrock, Embedding from HuggingFace and Vantage in-DB functions to assist in providing product recommendations to develop a recipe assistant chatbot.
Python Version
Mortgage Calculator Chatbot using GenAI: Finetune LLM
Fine-tuned an OpenAI model using RAG, LangChain and LLM models framework. Query a chatbot for answers about a mortgage and available housing within a predefiend area.
Python Version
Mortgage Calculator chatbot using GenAI: RAG
Build a conversational chatbot and ask questions about a mortgage and available housing within a predefined area using LangChain.
Python Version
Complaints Clustering with Google Gemini
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Geospatial
Supply Chain Visibility
Shows the integration of data from warehouse, trucking company, and customer Order Management Systems (OMS) using temporal to reconcile different update frequencies and show rerouting when warehouse is unavailable.
Python Version
4D Analytics on bike sharing
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Car Complaints
Uses geospatial techniques to locate service centers close to the complaint and outlier detection to detect part defects earlier than expected.
Python-SQL Version
Competitor Proximity Geospatial Analysis
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
Flood Proximity to Climate Risk Analysis
This allows enterprises to rapidly analyze this geographic-related information in real-time at any scale - effectively understanding the impacts of these climate events on entire populations.
Python Version
NYC Taxi Geospatial
Applies geospatial functions to analysis of NYC Taxi pickups and drop off locations.
Python-SQL Version
Sensor Data Analytics
Creative application of geospatial to locations of sensors in a research lab and integration of data from tables with detailed recordings on cloud storage.
Python-SQL Version
Telco Network Coverage
Demonstrates the ability of Geospatial to show signal strength, coverage areas and travel path of customers through cell tower coverage area.
Python Version
Hyperparameter Tuning
Hyperparameter Tuning using the Titanic Passenger Dataset
Create a predictive algorithm that can identify whethr or not passengers on the Titanic survived the ship's sinking.
Python Version
Hypothesis testing
Competitor Proximity Geospatial Analysis
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
Python Version
Data Science 101 with Python
This walks through the Cross-Industry Standard Process for Data Mining (CRISP-DM) from data understanding through modeling and evaluation.
Python Version
Sales Forecasting using UAF
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
Python Version
VAL Hypothesis Tests
This demonstrates a subset of the 18 hypothesis test in the Vantage Analytics library using SQL such as Parametric, Binomial, Kolmogorov/Smirnoff, Rank, etc.
SQL Version
Language Models
Initiate Parallel CPU Inferencing of Hugging Face Models in Vantage
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
Streamlining Analytics with Hyper-Segmented Models
By addressing unique customer behaviors and preferences within specific micro-segments, enhance customer satisfaction, boost operational efficiency, and improve profitability by to deploying and managing hyper-segmented models at scale.
Python Version
RAG Solution with Embedding/Chunking with Vantage and AWS Bedrock
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
RAG Solution with Vantage Model Catalog and AWS Bedrock
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Semantic Clustering using OpenSource Language Models
Bring a language model into Vantage and run NLP models in the database
Python Version
Semantic Similarity using OpenSource Language Models
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Large Language Models
Complaints Classification with OpenAI
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with OpenAI
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with OpenAI
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with OpenAI
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with OpenAI
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with AWS Bedrock
Analyzing consumer complaints using audio files conversations
Python Version
Speech Recognition and Sentiment Analysis with OpenAI
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with OpenAI
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Complaints Summarization with AWS Bedrock
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Complaints Classification with Google Gemini
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with Google Gemini
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with Google Gemini
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with Google Gemini
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with Google Gemini
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Complaints Clustering with AWS Bedrock
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Topic Modelling with AWS Bedrock
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer Complaints Analysis with Customer360 with AWS Bedrock
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Insights from Sentiment Analysis with AWS Bedrock
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Customer Complaints Analysis Dashboard with OpenAI
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Customer Complaint Analysis with Google Gemini
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
Customer Complaint Analysis with AWS Bedrock
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Machine learning
Anomaly Detection of Outstanding Amounts
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
Sales Forecasting with Vantage vs SAS
This demo walks through how a typical SAS user would use sales data to build a simple sales forecasting model and then will showcase how we can achieve the same using Vantage ClearScape Analytics.
Python-SQL Version SQL Version
Shipping Time Prediction
Use ClearScape Analytics to develop a robust system that can reliably estimate delivery dates, accounting for handling time, transit time, and other relevant variables affecting the actual delivery timeframe.
Python-SQL Version
Telco 5G Quality of Experience
Quality of service analysis of 5G vs 4G video for different situations such as Indoor, outdoor, and moble.
SQL Version
Anomaly Detection
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Banking Customer Churn
Uses a combination of Vantage Analytics Library to prepare data, using machine learning in python and importing the resulting PMML model into Vantage for scoring.
Python Version
Diabetes Prediction via DF and GLM
Decision Forest and Generalized Linear Model are applied to identify factors that indicate diabetes. The earlier the disease is identified, the better the chance of reducing organ damage.
Python Version
Financial Fraud Detection InDB
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version Video
Financial Fraud Detection VIA BYOM
A model that was developed externally is imported into Vantage for evaluation and execution at scale to detect fraud.
Python Version
Graph Analysis of CDR Records
Uses graph analysis to identify communities and key influencers within Call Data Records. This uses Script Table Operator to invoke external procedures to work inside the Vantage database without exporting data.
Python-SQL Version
Green Manufacturing
Uses analytic and ML techniques to predict how long vehicle testing will take based on combination of features installed.
Python Version
Heart Failure Prediction
Machine learning is applied to the complex attributes of patients to help recognize patterns that may lead to heart failure faster than a human may recognize.
Python-SQL Version
Hyper-Personalization
Hyper-personalization creates models from customer iteractions on multiple channels to determine the "Next Best Offer" for the individual.
Python-SQL Version
K-Means Clustering and ML model
This uses the unsupervised K-Means ML algorithm to identify patterns in retail purchases.
SQL Version
Marketing Campaign Effectiveness
Examines the results of campaigns by various customer attributes then uses correlation, outlier elimination, and machine learning to identify the best campaigns.
Python-SQL Version
Multi-Touch Attribution for Business Analyst
This version of the Multi-Touch Attribution demonstration is focused on the interests of the Business Analyst.
Python-SQL Version
Multi-Touch Attribution for Data Scientist
Demonstrates attribution of customer behavior via single touch and multi-touch rule-based models using statistical and algorithmic models. Multiple approaches are demonstrated.
Python-SQL Version
Predictive Maintenance
Uses ML functions to predict failures to identify and mitigate potential machine failures before they occur.
Python-SQL Version
Remaining Useful Life Forecasting
Applies machine learning to predict Remaining Useful Life (RUL) of jet engines, allowing scheduling of maintenance and replacement before failure occurs and reduces the cost of maintenance and replacement.
Python-SQL Version
Store Sales Forecast via ARIMA
Forecasts total store sales using ARIMA (AutoRegressive Integrated Moving Average)
Python Version
Store Sales Forecast via Prophet
Uses the Script Table Operator (STO) to run the H20 machine learning library as an extension to ClearScape Analytics. H20 runs in parallel inside the Vantage database without exporting data to another platform.
Python-SQL Version
Telco Customer Churn
This uses logistic regression for supervised learning to predict the probability of a customer switching vendors based on usage patterns, billing information demographics and interactions. XGBoost is then used in database to improve the prediction.
Python Version Video
Train Delay Path Analysis
Uses nPath displays to show relationship of delays and predictive models to anticipate potential delays and enable proactive planning.
Python Version
VAL Analytics and ML
Demonstration of Vantage Analytic Library scoring and analytic functions like linear regression, decision trees, K-Means clustering, Factor Analysis, etc.
SQL Version
Vertex AI Integration
In this tutorial, we will show how to integrate Vantage Analytics capabilities in Vertex AI ML Pipelines. Vertex AI is the environment for data scientists to deploy ML models.
Python Version
Data Quality Anomaly Detection using Statistical Techniques
Compare the distribution and variation of data between two time intervals usin In-DB function executed via a framework to provide alerts highlighting data anomalies.
Python Version
Hyperparameter Tuning using the Titanic Passenger Dataset
Create a predictive algorithm that can identify whethr or not passengers on the Titanic survived the ship's sinking.
Python Version
Anomaly Detection in Spot Welding Process - Trusted AI
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python Version
Cancer Prediction using the TDAPIClient and VertexAI
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Credit Risk Assessment using Teradataml OpenSource Functions
Use inDb functions with OpensourceML to create multiple DecisionTreeClassifiers to create multiple predictions of a Credit Risk Assessment.
Python Version
Financial Fraud Detection InDB
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
Predicting Medical Expenses in Healthcare
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
Resume Classification Model Training using Vantage In-DB Functions
Use data from a collection of Resume Examples taken from kaggle for categorizing a given resume into any of the labels defined in the dataset. Summarize and ask questions using a ChatBot.
Python Version
Cancer Prediction using Teradata and the SageMaker API
Use the Vantage SageMaker API feature to connect to an Amazon Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Parkinson's Disease Prediction using Signal Processing
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
ModelOps
ModelOps Introduction & List of Notebooks
This introduction and table of contents introduces you to ModelOps and provides a launch for ModelOps. It is recommended to go through ModelOps demonstrations in sequence.
Information
Natural Language Processing
Natural Language Processing
Use NLP for Sentiment Analysis, perform Kmeans clustering, execute Principal Component Analysis (PCA) using comments received by a Retail Store
Python-SQL Version
Object Storage
Battery Defect Analysis
Uses local data (or foreign tables on GCP) to analyze patterns of battery failure, then links to data on AWS for detailed battery measurement.
Python Version
Deep History via Object Store
This demonstrates integration of local data or foreign tables on GCP and integration across cloud providers to detailed historical sales records on AWS.
SQL Version
Sensor Data Analytics
Creative application of geospatial to locations of sensors in a research lab and integration of data from tables with detailed recordings on cloud storage.
Python-SQL Version
Open Source
Parkinson's Disease Prediction using Signal Processing
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
Semantic Clustering using OpenSource Language Models
Bring a language model into Vantage and run NLP models in the database
Python Version
Semantic Similarity using OpenSource Language Models
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Open-and-connected analytics
Vehicle Routing for Delivery
Demonstrates advanced analytics to find lowest cost routes to deliver a large volume of packages by a fleet of vehicles.
Python-SQL Version
Anomaly Detection
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Banking Customer Churn
Uses a combination of Vantage Analytics Library to prepare data, using machine learning in python and importing the resulting PMML model into Vantage for scoring.
Python Version
Path Analytics
Broken Digital Journey
This uses nPath® analysis to show the retail events that prevent the ultimate objective: a purchase. This uses interactive Sankey diagrams to understand the problems.
Python Version Video
Customer Behavior Analysis
Analysis of customer purchase behavior using nPath® analysis in Python with visualization using Sankey diagrams.
Python Version SQL Version
Open-and-connected analytics
Dataiku
Discusses how the 3rd party tool DataIku can be used with Vantage.
Information
Diabetes Prediction via BYOM H2O
This uses BYOM to import a trained H2O model to identify potential diabetes patients. BYOM allows the data scientist to create models in languages they prefer and run at scale inside Vantage without moving data.
Python-SQL Version
Energy Consumption Forecasting
This combines machine learning and BYOM to forecast energy consumption using Vantage to score the model at scale without having to export all data.
Python Version
Energy Consumption Forecasting Dataiku
Demonstration of using Dataiku with Vantage. Instructions provided for use with your Dataiku copy + screen shots if you don't have Dataiku. PMML model from Dataiku is imported to Vantage for execution and scoring.
Python Version
Path Analytics
Financial Customer Journey
Uses analytic techniques to find new customers, measure marketing attribution, and maximizing marketing effectiveness
Python-SQL Version SQL Version Video
Open-and-connected analytics
Financial Fraud Detection VIA BYOM
A model that was developed externally is imported into Vantage for evaluation and execution at scale to detect fraud.
Python Version
Path Analytics
Knee Replacement Attribution
The ClearScape Analytics Attribution function is used to determine the weight of various events that precede the final outcome, in this example, knee replacement.
SQL Version
Knee Replacement nPath
This uses the ClearScape Analytics nPath® function to provide visuals on the events leading up to the final outcome, in this case, knee replacement.
Python Version
Open-and-connected analytics
teradataml Python Basics
Introduction to Teradataml package for Python including connecting to Vantage, Teradata DataFrames, data manipulation and export to Pandas.
Python Version
Path Analytics
Train Delay Path Analysis
Uses nPath displays to show relationship of delays and predictive models to anticipate potential delays and enable proactive planning.
Python Version
Open-and-connected analytics
Vertex AI Integration
In this tutorial, we will show how to integrate Vantage Analytics capabilities in Vertex AI ML Pipelines. Vertex AI is the environment for data scientists to deploy ML models.
Python Version
Anomaly Detection in Spot Welding Process - Trusted AI
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python Version
PCA (Principal Component Analysis)
Customer Segmentation with K-means
Use K-means clustering to segment customers by purchase volume and value using R and tdplyr
R Version
Polarity Classification
Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Prediction Analysis
Cancer Prediction using the TDAPIClient and VertexAI
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Customer Segmentation with K-means
Use K-means clustering to segment customers by purchase volume and value using R and tdplyr
R Version
Cancer Prediction using Teradata and the SageMaker API
Use the Vantage SageMaker API feature to connect to an Amazon Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Sentiment Analysis
Insights from Sentiment Analysis with AWS Bedrock
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Customer Complaints Analysis with Customer360 with AWS Bedrock
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with OpenAI
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with OpenAI
Analyzing consumer complaints using audio files conversations
Python Version
Signal Processing
Parkinson's Disease Prediction using Signal Processing
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
Python Version
TeradataML Open Source Functions
Predicting Medical Expenses in Healthcare
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
Python Version
Text Analysis
Customer 360
This integrates data about customers from multiple sources using multiple matching techniquest to create the "Golden Customer Record" and calculate LTV.
SQL Version
Text Term Frequency
Use NGram splitter to analyze comments retail products to determine patterns of words used to describe products.
Python Version Python-SQL Version SQL Version
VAL Descriptive Statistics
This performs in-database analysis of data values, distribution, histograms, and text field analysis using SQL to access the Vantage Analytics Library.
SQL Version
Natural Language Processing
Use NLP for Sentiment Analysis, perform Kmeans clustering, execute Principal Component Analysis (PCA) using comments received by a Retail Store
Python-SQL Version
Time series analytics
Supply Chain Visibility
Shows the integration of data from warehouse, trucking company, and customer Order Management Systems (OMS) using temporal to reconcile different update frequencies and show rerouting when warehouse is unavailable.
Python Version
4D Analytics on bike sharing
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Air Passenger Forecasting
Applies Auto Regressive Integrated Moving Average (ARIMA) analysis to forecast airplane passenger volume.
SQL Version
Carbon Footprint Analytics
A key component of ESG is Carbon Footprint. This demonstrates a part of a solution available from Teradata to integrate multiple data sources to calculate carbon footprint of various corporate activities.
Python-SQL Version
Cash Demand Forecasing
Predicts the future demand for cast in Automatic Teller Machines (ATMs) using Auto Regressive Integrated Moving Average (ARIMA) using In-Database functions.
SQL Version
Consumer Complaints
Uses government consumer complaint data with SQL queries and visualizations to identify source of top complaints.
SQL Version
Energy Consumption Forecasting
This combines machine learning and BYOM to forecast energy consumption using Vantage to score the model at scale without having to export all data.
Python Version
Fourier Transforms
Fourier transformations are demonstrated to filter out noise from signals to allow identification of underlying patterns.
SQL Version
Insurance Policy Temporal
Show As-IS/As-Was capabilities of Vantage Temporal to dramatically simplify the SQL and improve performance for analyzing insurance policies versus claims.
SQL Version
NYC Taxi Temporal
Time series data can answer the questions about what was happening at a point in time. This applies Time series and temporal capabilities of vantage to NYC taxi data.
SQL Version
Retail Demand Forecasting
This creates an ARIMA time series model based on holidays and merchandising activities impacting store sales for a hypermarket retailer.
Python Version
Vantage Query Log Analysis
Analysis of sessions and queries you executed using the built-in logging facilities of Vantage.
SQL Version
Enhance Product Hierarchies with In-DB K-means Clustering
Enhance a product hierarchy by leveraging time series analysis and clustering techniques
Python Version
Topic Modelling
Customer Complaints Analysis with Customer360 with AWS Bedrock
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Topic Modelling with AWS Bedrock
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Topic Modelling with Google Gemini
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Topic Modelling with OpenAI
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Unbounded Array Framework
Signal Processing and Classification
Use the Unbounded Array Framework in ClearScape Analytics to classify sonar signals. Extract and engineer features to use in training and scoring our models.
Python Version
3rd Party Tools
AWS SageMaker
Cancer Prediction using Teradata and the SageMaker API
Use the Vantage SageMaker API feature to connect to an Amazon Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Generate Teradata SQL with GenAI and AWS Bedrock
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
Azure ML
Energy Consumption Forecasting using AzureML
This leverages the power of AzureML and Teradata Vantage to enhance our machine learning capabilities and enable scalable model scoring to forecast energy consumption.
Python Version
Celebrus
Digital Identity Management
Combines ClearScape Analytics with Celebrus to track unique customers across web sessions and devices to drive personalized experiences
SQL Version
Dataiku
Dataiku
Discusses how the 3rd party tool DataIku can be used with Vantage.
Information
Energy Consumption Forecasting Dataiku
Demonstration of using Dataiku with Vantage. Instructions provided for use with your Dataiku copy + screen shots if you don't have Dataiku. PMML model from Dataiku is imported to Vantage for execution and scoring.
Python Version
H2O.ai
ModelOps Introduction & List of Notebooks
This introduction and table of contents introduces you to ModelOps and provides a launch for ModelOps. It is recommended to go through ModelOps demonstrations in sequence.
Information
SAS
Sales Forecasting with Vantage vs SAS
This demo walks through how a typical SAS user would use sales data to build a simple sales forecasting model and then will showcase how we can achieve the same using Vantage ClearScape Analytics.
Python-SQL Version SQL Version
Vertex AI
Cancer Prediction using the TDAPIClient and VertexAI
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
Python Version
Vertex AI Integration
In this tutorial, we will show how to integrate Vantage Analytics capabilities in Vertex AI ML Pipelines. Vertex AI is the environment for data scientists to deploy ML models.
Python Version
AWS Bedrock
Complaints Classification with AWS Bedrock
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with AWS Bedrock
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with AWS Bedrock
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Insights from Sentiment Analysis with AWS Bedrock
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Topic Modelling with AWS Bedrock
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer Complaints Analysis with Customer360 with AWS Bedrock
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Product Recommendation via AWS Bedrock
Use AWS Bedrock, Embedding from HuggingFace and Vantage in-DB functions to assist in providing product recommendations to develop a recipe assistant chatbot.
Python Version
Speech Recognition and Sentiment Analysis with AWS Bedrock
Analyzing consumer complaints using audio files conversations
Python Version
Customer Complaint Analysis with AWS Bedrock
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Customer Reviews Analysis using GenAI
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
Generate Teradata SQL with GenAI and AWS Bedrock
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
Python Version
RAG Solution with Vantage Model Catalog and AWS Bedrock
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
RAG Solution with Embedding/Chunking with Vantage and AWS Bedrock
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
OpenAI
Customer Complaints Analysis Dashboard with OpenAI
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Using ClearScape Analytics with openAI
To ensure optimal utilization of the OpenAI API in generative AI notebooks, it is essential to establish the API keys correctly. This concise guide outlines the process of configuring OpenAI API keys for seamless integration across multiple notebooks.
Information
Getting Started with Azure OpenAI
Follow these instructions to setup the Azure OpenAI endpoint and generate the access Keys required to run the model.
Python Version
Mental Health Chat with Fine-tuned OpenAI Model
Train a GPT-3.5 Turbo model using the OpenAI API endpoint. Ask mental health questions using a chat input box.
Python Version
Mortgage Calculator Chatbot using GenAI: Finetune LLM
Fine-tuned an OpenAI model using RAG, LangChain and LLM models framework. Query a chatbot for answers about a mortgage and available housing within a predefiend area.
Python Version
Mortgage Calculator chatbot using GenAI: RAG
Build a conversational chatbot and ask questions about a mortgage and available housing within a predefined area using LangChain.
Python Version
Resume Classification Model Training using Vantage In-DB Functions
Use data from a collection of Resume Examples taken from kaggle for categorizing a given resume into any of the labels defined in the dataset. Summarize and ask questions using a ChatBot.
Python Version
Mortgage Calculator Chatbot using Trusted AI(RAG)
Experience the integration of LLM models to provide user-friendly responses to queries. RAG combines retrieval and generative approaches.
Python Version
Customer Reviews Analysis using GenAI
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
Python Version
Complaints Classification with OpenAI
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with OpenAI
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with OpenAI
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with OpenAI
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with OpenAI
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with OpenAI
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with OpenAI
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Google Gemini
Customer Complaint Analysis with Google Gemini
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
Complaints Classification with Google Gemini
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with Google Gemini
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with Google Gemini
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with Google Gemini
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with Google Gemini
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Hugging Face
Initiate Parallel CPU Inferencing of Hugging Face Models in Vantage
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
RAG Solution with Embedding/Chunking with Vantage and AWS Bedrock
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
RAG Solution with Vantage Model Catalog and AWS Bedrock
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Semantic Clustering using OpenSource Language Models
Bring a language model into Vantage and run NLP models in the database
Python Version
Semantic Similarity using OpenSource Language Models
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Language
Python
Anomaly Detection of Outstanding Amounts
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
Charting and Visualizations using teradataml
The td_plot method streamlines large-scale visualizations by providing users with efficient tools to create visualizations directly within the Vantage platform
Python Version
Chatbot using GenAI with insurance PDF document
Uses TD_VectorDistance in Vantage to create a chatbot using Retrieval-Augmented Generation (RAG), Langchain, and LLM to answer questions using a 24 page travelers insurance policy PDF as source data to answer questions about coverage.
Python Version Python-SQL Version
Credit Card Data Preparation
Uses functions from TeradataML Python library to prepare data for analysis using data cleansing, exploration and feature engineering functions.
Python Version
ModelOps Introduction & List of Notebooks
This introduction and table of contents introduces you to ModelOps and provides a launch for ModelOps. It is recommended to go through ModelOps demonstrations in sequence.
Information
Mortgage Calculator using GenAI
Use Retrieval-Augmented Generation (RAG), Lanchain and LLM models to as questions about loans and retrieve relevant data from Vantage.
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Product Recommentations via TDApiClient
Build a product recommendation system using OpenAI embeddings and Vantage in db_function VectorDistance. We will also use Vantage as VectorDB, to store the embeddings.
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Sales Forecasting with Vantage vs SAS
This demo walks through how a typical SAS user would use sales data to build a simple sales forecasting model and then will showcase how we can achieve the same using Vantage ClearScape Analytics.
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Shipping Time Prediction
Use ClearScape Analytics to develop a robust system that can reliably estimate delivery dates, accounting for handling time, transit time, and other relevant variables affecting the actual delivery timeframe.
Python-SQL Version
Supply Chain Visibility
Shows the integration of data from warehouse, trucking company, and customer Order Management Systems (OMS) using temporal to reconcile different update frequencies and show rerouting when warehouse is unavailable.
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Vehicle Routing for Delivery
Demonstrates advanced analytics to find lowest cost routes to deliver a large volume of packages by a fleet of vehicles.
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Grocery Recommendations using GenAI
Use context-based product recommendations powered by GenAI Large Language Models to enhance a shopping experience.
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Hyperparameter Tuning using the Titanic Passenger Dataset
Create a predictive algorithm that can identify whethr or not passengers on the Titanic survived the ship's sinking.
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Signal Processing and Classification
Use the Unbounded Array Framework in ClearScape Analytics to classify sonar signals. Extract and engineer features to use in training and scoring our models.
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Customer Complaint Analysis with Google Gemini
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
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Getting Started with Azure OpenAI
Follow these instructions to setup the Azure OpenAI endpoint and generate the access Keys required to run the model.
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Natural Language Processing
Use NLP for Sentiment Analysis, perform Kmeans clustering, execute Principal Component Analysis (PCA) using comments received by a Retail Store
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Speech Recognition and Sentiment Analysis with AWS Bedrock
Analyzing consumer complaints using audio files conversations
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Tutorial on using Teradataml Widgets
Use Teradataml Widgets to display interactive prompting to generate datasets from the Vantage database.
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Convert PySpark to teradatamlspk
Convert a PySpark script to teradatamlspk syntax and generate a HTML report using Housing Prices to generate price predictions.
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Customer Complaints Analysis with Customer360 with AWS Bedrock
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
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Generate Teradata SQL with GenAI and AWS Bedrock
In this demo, we use AWS Bedrock's LLMs and LangChain to create a text-to-Teradata SQL agent.
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Introduction to Plot types using Teradataml Widgets.
This is an introduction to using the various Plot types available as widgets: Line, Bar, Mesh, Wiggle, Geometry, etc.
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Sales Forecasting using Teradata AUTOARIMA
Forecast the amount of future sales by developing a time-series modelling pipeline using Vantage and In-DB analytics
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Streamlining Analytics with Hyper-Segmented Models
By addressing unique customer behaviors and preferences within specific micro-segments, enhance customer satisfaction, boost operational efficiency, and improve profitability by to deploying and managing hyper-segmented models at scale.
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Banking Churn Prediction with AutoML
Implement the entire lifecycle of churn prediction using BYOM, VAL and AutoML.
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Complaints Classification with OpenAI
Classify customer complaints to swiftly identify and address concerns.
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Complaints Clustering with AWS Bedrock
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
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Complaints Clustering with OpenAI
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
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Complaints Summarization with OpenAI
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
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Credit Risk Assessment using Teradataml OpenSource Functions
Use inDb functions with OpensourceML to create multiple DecisionTreeClassifiers to create multiple predictions of a Credit Risk Assessment.
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Customer Complaints Analysis with Customer360 with OpenAI
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
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Customer Reviews Analysis using GenAI
Customer reviews analysis is a crucial aspect of understanding customer sentiment and preferences. By leveraging the power of OpenAIEmbeddings and Vantage InDB Analytic Function, we can gain valuable insights from customer reviews.
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Data Quality Anomaly Detection using Statistical Techniques
Compare the distribution and variation of data between two time intervals usin In-DB function executed via a framework to provide alerts highlighting data anomalies.
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Execute Custom Python Scripts in Vantage
Run an external custom python script in Vantage using Script Table Operator(STO) to execute custom python scripts on Vantage
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Mental Health Chat with Fine-tuned OpenAI Model
Train a GPT-3.5 Turbo model using the OpenAI API endpoint. Ask mental health questions using a chat input box.
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Mortgage Calculator chatbot using GenAI: RAG
Build a conversational chatbot and ask questions about a mortgage and available housing within a predefined area using LangChain.
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Mortgage Calculator Chatbot using Trusted AI(RAG)
Experience the integration of LLM models to provide user-friendly responses to queries. RAG combines retrieval and generative approaches.
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Parkinson's Disease Prediction using Signal Processing
Detect Parkinson's Disease at an early stage by using Vantage InDB functions for model training and scoring to compare the performance of two models against biomedical voice measurements.
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Sentiment Analysis with OpenAI
Extract insights from unstructured data to identify and address customer concerns.
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Speech Recognition and Sentiment Analysis with OpenAI
Analyzing consumer complaints using audio files conversations
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Topic Modelling with OpenAI
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
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Anomaly Detection in Spot Welding Process - Trusted AI
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
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Cancer Prediction using the TDAPIClient and VertexAI
Use TDAPIClient to connect to the Google Cloud Vertex API Endpoint to orchestrate Extreme Gradient XG Boost model training and score the model in SageMaker. Deploy the solution's ML model.
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Customer Complaints Analysis Dashboard with OpenAI
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Product Recommendation via AWS Bedrock
Use AWS Bedrock, Embedding from HuggingFace and Vantage in-DB functions to assist in providing product recommendations to develop a recipe assistant chatbot.
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Resume Classification Model Training using Vantage In-DB Functions
Use data from a collection of Resume Examples taken from kaggle for categorizing a given resume into any of the labels defined in the dataset. Summarize and ask questions using a ChatBot.
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Customer Complaint Analysis with AWS Bedrock
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Financial Fraud Detection InDB
Detect financial transaction fraud using powerful in-database machine learning functions
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Initiate Parallel CPU Inferencing of Hugging Face Models in Vantage
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
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Predicting Medical Expenses in Healthcare
Use a dataset containing variables like age, sex, BMI, smoking status, number of children, and region to build machine learning models that accurately predict healthcare costs for insurance policyholders, taking into account factors that affect medical expenses.
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RAG Solution with Embedding/Chunking with Vantage and AWS Bedrock
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
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RAG Solution with Vantage Model Catalog and AWS Bedrock
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
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Semantic Clustering using OpenSource Language Models
Bring a language model into Vantage and run NLP models in the database
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Semantic Similarity using OpenSource Language Models
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
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Complaints Classification with AWS Bedrock
Classify customer complaints to swiftly identify and address concerns.
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Complaints Classification with Google Gemini
Classify customer complaints to swiftly identify and address concerns.
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Complaints Clustering with Google Gemini
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
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Complaints Summarization with Google Gemini
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
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Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
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Speech Recognition and Sentiment Analysis with Google Gemini
Analyzing consumer complaints using audio files conversations
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Topic Modelling with AWS Bedrock
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
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Topic Modelling with Google Gemini
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
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Insights from Sentiment Analysis with AWS Bedrock
Extract insights from unstructured data to identify and address customer concerns.
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Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
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4D Analytics on bike sharing
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Anomaly Detection
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Banking Customer Churn
Uses a combination of Vantage Analytics Library to prepare data, using machine learning in python and importing the resulting PMML model into Vantage for scoring.
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Battery Defect Analysis
Uses local data (or foreign tables on GCP) to analyze patterns of battery failure, then links to data on AWS for detailed battery measurement.
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Broken Digital Journey
This uses nPath® analysis to show the retail events that prevent the ultimate objective: a purchase. This uses interactive Sankey diagrams to understand the problems.
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Car Complaints
Uses geospatial techniques to locate service centers close to the complaint and outlier detection to detect part defects earlier than expected.
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Carbon Footprint Analytics
A key component of ESG is Carbon Footprint. This demonstrates a part of a solution available from Teradata to integrate multiple data sources to calculate carbon footprint of various corporate activities.
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Competitor Proximity Geospatial Analysis
This looks at the impact on purchasing when people that shop at our stores live within reasonable drive time to a competitor. This could identify proximity based marketing tactics to target larger promotions to those customers to increase share of their spend.
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Customer Behavior Analysis
Analysis of customer purchase behavior using nPath® analysis in Python with visualization using Sankey diagrams.
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Data Dictionary
This provides an index to all of the databases used by demo notebooks on this machine, allowing you to use that data for your own notebooks or BI tools.
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Data Dictionary Raw
This provides linkage to a larger set of databases and tables than are currently used by the demos in Jupyter.
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Data Loading (Python)
Shows how to use python to load CSV data from local storage and from zipped files
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Data Prep and Transformation
This demonstrates a subset of the over 100 analytic functions in the teradataml package for Python
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Data Science 101 with Python
This walks through the Cross-Industry Standard Process for Data Mining (CRISP-DM) from data understanding through modeling and evaluation.
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Diabetes Prediction via BYOM H2O
This uses BYOM to import a trained H2O model to identify potential diabetes patients. BYOM allows the data scientist to create models in languages they prefer and run at scale inside Vantage without moving data.
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Diabetes Prediction via DF and GLM
Decision Forest and Generalized Linear Model are applied to identify factors that indicate diabetes. The earlier the disease is identified, the better the chance of reducing organ damage.
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Energy Consumption Forecasting
This combines machine learning and BYOM to forecast energy consumption using Vantage to score the model at scale without having to export all data.
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Energy Consumption Forecasting Dataiku
Demonstration of using Dataiku with Vantage. Instructions provided for use with your Dataiku copy + screen shots if you don't have Dataiku. PMML model from Dataiku is imported to Vantage for execution and scoring.
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Energy Consumption Forecasting using AzureML
This leverages the power of AzureML and Teradata Vantage to enhance our machine learning capabilities and enable scalable model scoring to forecast energy consumption.
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Financial Customer Journey
Uses analytic techniques to find new customers, measure marketing attribution, and maximizing marketing effectiveness
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Financial Fraud Detection InDB
Detect financial transaction fraud using powerful in-database machine learning functions
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Financial Fraud Detection VIA BYOM
A model that was developed externally is imported into Vantage for evaluation and execution at scale to detect fraud.
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Flood Proximity to Climate Risk Analysis
This allows enterprises to rapidly analyze this geographic-related information in real-time at any scale - effectively understanding the impacts of these climate events on entire populations.
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Generative AI Question Answering
Uses OpenAI to translate english language questions into SQL queries to run against a table of marketing data. This includes a link to a notebook with instructions for getting the required OpenAI API key.
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Graph Analysis of CDR Records
Uses graph analysis to identify communities and key influencers within Call Data Records. This uses Script Table Operator to invoke external procedures to work inside the Vantage database without exporting data.
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Green Manufacturing
Uses analytic and ML techniques to predict how long vehicle testing will take based on combination of features installed.
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Heart Failure Prediction
Machine learning is applied to the complex attributes of patients to help recognize patterns that may lead to heart failure faster than a human may recognize.
Python-SQL Version
How to Submit Your Demos
It is very easy to submit your demo for publication. Tell us directory with the notebook and referenced files and grant us access to your database. We'll take it from there.
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Hyper-Personalization
Hyper-personalization creates models from customer iteractions on multiple channels to determine the "Next Best Offer" for the individual.
Python-SQL Version
Intro to Panda for Python
Provides step-by-step instructions on the basics of using Python Pandas with Jupyter notebooks.
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Knee Replacement nPath
This uses the ClearScape Analytics nPath® function to provide visuals on the events leading up to the final outcome, in this case, knee replacement.
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Marketing Campaign Effectiveness
Examines the results of campaigns by various customer attributes then uses correlation, outlier elimination, and machine learning to identify the best campaigns.
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Multi-Touch Attribution for Business Analyst
This version of the Multi-Touch Attribution demonstration is focused on the interests of the Business Analyst.
Python-SQL Version
Multi-Touch Attribution for Data Scientist
Demonstrates attribution of customer behavior via single touch and multi-touch rule-based models using statistical and algorithmic models. Multiple approaches are demonstrated.
Python-SQL Version
NYC Taxi Geospatial
Applies geospatial functions to analysis of NYC Taxi pickups and drop off locations.
Python-SQL Version
Predictive Maintenance
Uses ML functions to predict failures to identify and mitigate potential machine failures before they occur.
Python-SQL Version
Product Recommendations via Open Source
Uses FlagEmbedding from HuggingFace plus Vantage in-database functions to assess the vector distance between the product entered and similar products.
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Product Recommendations via OpenAI
Uses OpenAIEmbeddings and Vantage in-database function to assists consumers in receiving product recommendations
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Query Service REST API
Demonstration of using REST API calls to Vantage which is useful for web or mobile applications to access and maintain data.
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Remaining Useful Life Forecasting
Applies machine learning to predict Remaining Useful Life (RUL) of jet engines, allowing scheduling of maintenance and replacement before failure occurs and reduces the cost of maintenance and replacement.
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Retail Demand Forecasting
This creates an ARIMA time series model based on holidays and merchandising activities impacting store sales for a hypermarket retailer.
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Sales Forecasting using UAF
A detailed coverage of the analytic steps in sales forecasting including data preparation, exploration, seasonal normalizing, model creation, validation, and forecasting.
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Sensor Data Analytics
Creative application of geospatial to locations of sensors in a research lab and integration of data from tables with detailed recordings on cloud storage.
Python-SQL Version
Store Sales Forecast via ARIMA
Forecasts total store sales using ARIMA (AutoRegressive Integrated Moving Average)
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Store Sales Forecast via Prophet
Uses the Script Table Operator (STO) to run the H20 machine learning library as an extension to ClearScape Analytics. H20 runs in parallel inside the Vantage database without exporting data to another platform.
Python-SQL Version
Telco Customer Churn
This uses logistic regression for supervised learning to predict the probability of a customer switching vendors based on usage patterns, billing information demographics and interactions. XGBoost is then used in database to improve the prediction.
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Telco Network Coverage
Demonstrates the ability of Geospatial to show signal strength, coverage areas and travel path of customers through cell tower coverage area.
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teradataml Python Basics
Introduction to Teradataml package for Python including connecting to Vantage, Teradata DataFrames, data manipulation and export to Pandas.
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Text Term Frequency
Use NGram splitter to analyze comments retail products to determine patterns of words used to describe products.
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Train Delay Path Analysis
Uses nPath displays to show relationship of delays and predictive models to anticipate potential delays and enable proactive planning.
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VAL teradataml Demo
Demonstrated the use of Teradataml in Python to perform descriptive statistics, transformation, model building model evaluation and scoring.
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Vertex AI Integration
In this tutorial, we will show how to integrate Vantage Analytics capabilities in Vertex AI ML Pipelines. Vertex AI is the environment for data scientists to deploy ML models.
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SQL
Chatbot using GenAI with insurance PDF document
Uses TD_VectorDistance in Vantage to create a chatbot using Retrieval-Augmented Generation (RAG), Langchain, and LLM to answer questions using a 24 page travelers insurance policy PDF as source data to answer questions about coverage.
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Sales Forecasting with Vantage vs SAS
This demo walks through how a typical SAS user would use sales data to build a simple sales forecasting model and then will showcase how we can achieve the same using Vantage ClearScape Analytics.
Python-SQL Version SQL Version
Shipping Time Prediction
Use ClearScape Analytics to develop a robust system that can reliably estimate delivery dates, accounting for handling time, transit time, and other relevant variables affecting the actual delivery timeframe.
Python-SQL Version
Telco 5G Quality of Experience
Quality of service analysis of 5G vs 4G video for different situations such as Indoor, outdoor, and moble.
SQL Version
Customer Behavior Analysis
Analysis of customer purchase behavior using nPath® analysis in Python with visualization using Sankey diagrams.
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Grocery Recommendations using GenAI
Use context-based product recommendations powered by GenAI Large Language Models to enhance a shopping experience.
Python-SQL Version
4D Analytics on bike sharing
Demonstration of Geospatial and TimeSeries using Austin bike trip data between 2014 and 2017.
Python-SQL Version SQL Version
Air Passenger Forecasting
Applies Auto Regressive Integrated Moving Average (ARIMA) analysis to forecast airplane passenger volume.
SQL Version
Basic Jupyter Navigation
When running a Jupyter Notebook, there are various indicators that show what is happening. This is a guide to those indicators.
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Cash Demand Forecasing
Predicts the future demand for cast in Automatic Teller Machines (ATMs) using Auto Regressive Integrated Moving Average (ARIMA) using In-Database functions.
SQL Version
Charting and Visualization
Data from queries is brought to life with graphics and charts. This shows how to use the %chart magic command to display results.
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Consumer Complaints
Uses government consumer complaint data with SQL queries and visualizations to identify source of top complaints.
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Credit Card Data Preparation
This shows the use of ClearScape analytics to reduce the pre-processing effort of incoming raw credit card data to prepare for analysis of potential loan defaulters.
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Customer 360
This integrates data about customers from multiple sources using multiple matching techniquest to create the "Golden Customer Record" and calculate LTV.
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Data Loading (SQL)
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
SQL Version
Deep History via Object Store
This demonstrates integration of local data or foreign tables on GCP and integration across cloud providers to detailed historical sales records on AWS.
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Digital Identity Management
Combines ClearScape Analytics with Celebrus to track unique customers across web sessions and devices to drive personalized experiences
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Financial Customer Journey
Uses analytic techniques to find new customers, measure marketing attribution, and maximizing marketing effectiveness
Python-SQL Version SQL Version Video
Fourier Transforms
Fourier transformations are demonstrated to filter out noise from signals to allow identification of underlying patterns.
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Insurance Policy Temporal
Show As-IS/As-Was capabilities of Vantage Temporal to dramatically simplify the SQL and improve performance for analyzing insurance policies versus claims.
SQL Version
K-Means Clustering and ML model
This uses the unsupervised K-Means ML algorithm to identify patterns in retail purchases.
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Knee Replacement Attribution
The ClearScape Analytics Attribution function is used to determine the weight of various events that precede the final outcome, in this example, knee replacement.
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NYC Taxi Temporal
Time series data can answer the questions about what was happening at a point in time. This applies Time series and temporal capabilities of vantage to NYC taxi data.
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Outlier Analysis
Outliers in an analysis skew the results and make it difficult to recognize the main patterns. ClearScape Analytics has tools to remove outliers automatically.
SQL Version
SQL Basics in Jupyter
This guide will walk you through writing your first SQL queries in Jupyter. It uses some of the Vantage system tables as a source for the queries.
SQL Version
Text Term Frequency
Use NGram splitter to analyze comments retail products to determine patterns of words used to describe products.
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VAL Analytics and ML
Demonstration of Vantage Analytic Library scoring and analytic functions like linear regression, decision trees, K-Means clustering, Factor Analysis, etc.
SQL Version
VAL Descriptive Statistics
This performs in-database analysis of data values, distribution, histograms, and text field analysis using SQL to access the Vantage Analytics Library.
SQL Version
VAL Hypothesis Tests
This demonstrates a subset of the 18 hypothesis test in the Vantage Analytics library using SQL such as Parametric, Binomial, Kolmogorov/Smirnoff, Rank, etc.
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VAL Overview
Vantage Analytics Library (VAL) is a set of over 50 functions for advanced analytics. This provides an overview and links to an 8 minute video overview.
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Vantage Query Log Analysis
Analysis of sessions and queries you executed using the built-in logging facilities of Vantage.
SQL Version
R
ModelOps Introduction & List of Notebooks
This introduction and table of contents introduces you to ModelOps and provides a launch for ModelOps. It is recommended to go through ModelOps demonstrations in sequence.
Information
Customer Segmentation with K-means
Use K-means clustering to segment customers by purchase volume and value using R and tdplyr
R Version
Teradata Package for R Basics
Discoverer how the Teradata Package for R (tdplyr) allows users to develop and run R programs to take advantage of Big Data and Machine Learning analytic capabilities of Vantage.
Information
Financial Fraud Detection InDB
Detect financial transaction fraud using powerful in-database machine learning functions
Python Version R Version
tdplyr R Basics
Work through using the bgasics of the Teradata R package, tdplyr
R Version
Data Loading ('R')
Shows multiple ways to load data from local CSV files, and cloud files on Google and AWS using the SQL kernel.
R Version
Solution Accelerator
Anomaly Detection
Anomaly Detection of Outstanding Amounts
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
Anomaly Detection
Evaluates potential failures in spot welds based on voltage anomalies during the welding process.
Python-SQL Version Video
Data Quality Anomaly Detection using Statistical Techniques
Compare the distribution and variation of data between two time intervals usin In-DB function executed via a framework to provide alerts highlighting data anomalies.
Python Version
Customer Complaint Analysis
Customer Complaint Analysis with AWS Bedrock
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Customer Complaints Analysis with Customer360 with AWS Bedrock
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Customer Complaints Analysis Dashboard with OpenAI
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Complaints Classification with AWS Bedrock
Classify customer complaints to swiftly identify and address concerns.
Python Version
Customer Complaint Analysis with Google Gemini
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
Topic Modelling with AWS Bedrock
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Complaints Classification with Google Gemini
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with Google Gemini
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with AWS Bedrock
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Complaints Summarization with Google Gemini
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with AWS Bedrock
Analyzing consumer complaints using audio files conversations
Python Version
Speech Recognition and Sentiment Analysis with Google Gemini
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with Google Gemini
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Complaints Classification with OpenAI
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with AWS Bedrock
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Clustering with OpenAI
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with OpenAI
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with OpenAI
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Insights from Sentiment Analysis with AWS Bedrock
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Sentiment Analysis with OpenAI
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with OpenAI
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with OpenAI
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Enterprise Feature Store
Anomaly Detection of Outstanding Amounts
Anomaly detection in bank accounts can help in identifying unusual patterns, potentially flagging issues like errors or fraudulent activities. Enterprise re-use of Features ensures rapid creation & deployment of models while new Features can be created and used without extensive engineering support.
Python Version
Other
New
Enhance Product Hierarchies with In-DB K-means Clustering
Enhance a product hierarchy by leveraging time series analysis and clustering techniques
Python Version
Execute Custom Python Scripts in Vantage
Run an external custom python script in Vantage using Script Table Operator(STO) to execute custom python scripts on Vantage
Python Version
Resume Classification Model Training using Vantage In-DB Functions
Use data from a collection of Resume Examples taken from kaggle for categorizing a given resume into any of the labels defined in the dataset. Summarize and ask questions using a ChatBot.
Python Version
Sales Forecasting using Teradata AUTOARIMA
Forecast the amount of future sales by developing a time-series modelling pipeline using Vantage and In-DB analytics
Python Version
Streamlining Analytics with Hyper-Segmented Models
By addressing unique customer behaviors and preferences within specific micro-segments, enhance customer satisfaction, boost operational efficiency, and improve profitability by to deploying and managing hyper-segmented models at scale.
Python Version
Parallel CPU Inferencing
RAG Solution with Embedding/Chunking with Vantage and AWS Bedrock
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
RAG Solution with Vantage Model Catalog and AWS Bedrock
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Semantic Clustering using OpenSource Language Models
Bring a language model into Vantage and run NLP models in the database
Python Version
Semantic Similarity using OpenSource Language Models
Run Hugging Face Embedding Model in ONNX format to create some embeddings from a Consumer Complaints dataset
Python Version
Initiate Parallel CPU Inferencing of Hugging Face Models in Vantage
Execute this notebook first to prepare the environment to demonstrate Parallel CPU Inferencing of Hugging Face Models in Vantage
Python Version
Dashboard Notebook
Resume Classification Model Training using Vantage In-DB Functions
Use data from a collection of Resume Examples taken from kaggle for categorizing a given resume into any of the labels defined in the dataset. Summarize and ask questions using a ChatBot.
Python Version
Customer Complaint Analysis with Google Gemini
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
Information
Customer Complaint Analysis with AWS Bedrock
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Customer Complaints Analysis Dashboard with OpenAI
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
Information
Cloud Provider
AWS
Insights from Sentiment Analysis with AWS Bedrock
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Complaints Classification with AWS Bedrock
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with AWS Bedrock
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with AWS Bedrock
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Topic Modelling with AWS Bedrock
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer Complaints Analysis with Customer360 with AWS Bedrock
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Speech Recognition and Sentiment Analysis with AWS Bedrock
Analyzing consumer complaints using audio files conversations
Python Version
RAG Solution with Embedding/Chunking with Vantage and AWS Bedrock
Chunk PDFs, run embedding, try vector db style indexing in Vantage and then query Language Model with context/prompts after semantic search
Python Version
RAG Solution with Vantage Model Catalog and AWS Bedrock
Explore how to do extract data from metadata tables of Teradata using embedding and vector db style indexing in Vantage and then query LLM with context/prompts to get the details
Python Version
Customer Complaint Analysis with AWS Bedrock
This is a collection of demos showing multiple methods of dealing with customer complaints using various GenAI techniques.
Information
Complaints Classification with Google Gemini
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with Google Gemini
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with Google Gemini
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with Google Gemini
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with Google Gemini
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with Google Gemini
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with Google Gemini
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version
Customer Complaint Analysis with Google Gemini
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Google Gemini.
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Azure
Customer Complaints Analysis Dashboard with OpenAI
This is a Dashboard with descriptions and links to various notebooks on various topics using Teradata Vantage and Microsoft Azure.
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Getting Started With Azure
Follow these steps to create your first Azure account and create an Azure Machine Learning workspace to complete our AzureML use cases.
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Getting Started with Azure OpenAI
Follow these instructions to setup the Azure OpenAI endpoint and generate the access Keys required to run the model.
Python Version
Complaints Classification with OpenAI
Classify customer complaints to swiftly identify and address concerns.
Python Version
Complaints Clustering with OpenAI
Use advanced clustering techniques using AI Embeddings model to group similar customer complaints together.
Python Version
Complaints Summarization with OpenAI
Efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.
Python Version
Customer Complaints Analysis with Customer360 with OpenAI
Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view
Python Version
Sentiment Analysis with OpenAI
Extract insights from unstructured data to identify and address customer concerns.
Python Version
Speech Recognition and Sentiment Analysis with OpenAI
Analyzing consumer complaints using audio files conversations
Python Version
Topic Modelling with OpenAI
Uncover hidden insights from vast amounts of consumer complaints data to enable the identification of trends.
Python Version