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5 years ago ADT201-L - Leadership Session: Digital Advertising - Customer Learning & the Road Ahead
In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack.
PowerPoint Presentation
ADT202 - Use Amazon Rekognition to Power Video Creative Asset Production
In this session, hear from an AWS customer about how they leveraged Amazon Rekognition deep learning-based image and video analysis to power a data-driven decision system for creative asset production. Learn how this customer was able to leverage the raw data provided by Amazon Rekognition combined with performance data to discover actionable insights. See a demonstration of the solution, and hear about media- and advertising-specific use cases. Learn from the customer's experiences implementing their architecture, the challenges, and the pleasant surprises along the way.
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AWS re:Invent 2018: Use Amazon Rekognition to Power Video Creative Asset Production (ADT202)
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ADT302 - Democratize Data Preparation for Analytics & Machine Learning: A Hands-On Lab
Machine learning (ML) outcomes are only as good as the data they are built upon. Preparing data for ML is time consuming and cumbersome; “data wrangling” for analytics can consume over 80% of project effort. ML Wrangling Assistant, based on Trifacta running on AWS, streamlines ML applications so teams can focus on the work that matters—creating accurate predictions that improve products, services, and organizational efficiency. In this lab, we cover one of two data preparation use cases. Marketing Analytics analyzes web ads by cleaning and transforming ecommerce transactions in a relational table combined to a clickstream semi-structured log file. Cross-Sell Analytics explores, structures, standardizes, and combines multiple file types (CSV, JSON, Excel) to create a single, consistent view of customers. Final outputs are the categorical features and attributes to train, test, and validate the data sets required by Amazon SageMaker to perform ML modeling.
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AIM203-S - Patient-Focused Data Science: Machine Learning for Complex Diseases
Curious about how Amazon machine learning (ML) services can enable healthcare organizations to find the insights they need to survive and thrive? Join us to learn how Takeda researchers built and trained their own disease-specific ML models, including deep-learning models using Deloitte ConvergeHEALTH running on AWS to simulate and quantify the overall disease burden and identify potential risks. This session is brought to you by AWS partner, Deloitte Consulting LLP.
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AIM204-S - Smarter Event-Driven Edge with Amazon SageMaker & Project Flogo
A single device can produce thousands of events every second. In traditional implementations, all data is transmitted back to a server or gateway for scoring by a machine learning (ML) model. This data is also stored in a data repository for later use by data scientists. In this session, we explore data science techniques for dealing with time series data leveraging Amazon SageMaker. We also look at modeling applications using deterministic rules with streaming pipelines for data prep, and model inferencing using deep learning frameworks directly onto edge devices or onto AWS Lambda using Project Flogo, an open-source event-driven framework. This session is brought to you by AWS partner, TIBCO Software Inc.
PowerPoint Presentation
AWS re:Invent 2018: Smarter Event-Driven Edge with Amazon SageMaker & Project Flogo (AIM204-S)
AIM205 - New AI/ML Solutions with AWS DeepLens & Amazon SageMaker with ConocoPhillips
ConocoPhillips is exploring the combination of machine vision and machine learning. Four proof of concepts were developed using AWS DeepLens, Amazon SageMaker, Amazon S3, and more. These projects address the security, safety, and inventory associated with upstream field operations. In this session, we describe our successes, challenges, and lessons learned. We also share our ideas for future product improvements.
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AIM207-S - Faster, Better, Cheaper: AI Apps in One-Tenth the Time and Cost
In this session, learn how the C3 Platform on AWS is architected and why it accelerates the development of enterprise-scale AI applications. Hear how customers like the US Air Force, Enel, and global manufacturing leaders are using C3 on AWS to rapidly aggregate, unify, federate, and normalize data from sensor networks and enterprise IT systems, and apply ML/AI algorithms against this data to unlock significant economic value. Hear from global organizations that are solving complex business challenges, from optimizing the supply network, to predicting which assets will fail, to identifying fraud and money laundering. This session is brought to you by AWS partner, C3.
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AIM208-S - Accelerating Enterprise-Scale AI Application Development
In this session, learn how the C3 Platform on AWS is architected to accelerate the development of modern AI applications. Hear how customers and partners have used the C3 Type System’s data-object centric abstraction layer to realize 10–100x productivity gains when building complex AI/ML applications. In addition, hear how global organizations are using C3 on AWS to solve complex business challenges, from optimizing the supply network, to predicting asset failure, to identifying fraud and money laundering. This presentation is brought to you by AWS partner, C3.
AWS re:Invent 2018: Accelerating Enterprise-Scale AI Application Development (AIM208-S)
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AIM302 - Machine Learning at the Edge
Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity.
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https://www.slideshare.net/AmazonWebServices/machine-learning-at-the-edge-aim302-aws-reinvent-2018
AIM307-R - [REPEAT] Deep Dive on Amazon Rekognition, ft. Tinder & News UK
Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications.
PowerPoint Presentation
AIM311 - Machine Learning and Predictive Quality Management
From refined products to heavy crude, Four-Path Ultrasonic Flow Meters offers the capability to minimize measurement uncertainty of liquid hydrocarbons. Attendees work to build a machine learning (ML) predictive quality management (PQM) solution on AWS to proactively predict the health of the ultrasonic flow meters. This is done using the ML Data Readiness Package based on KNIME, from AWS Marketplace. Another PQM example for attendees to explore uses features extracted from motor current measured with a current probe and an oscilloscope on two phases measured under different speeds, load moments, and load forces. ML is used to proactively classify whether the motor has intact or defective components. A third PQM example involves using raw process sensor data from a hydraulic test rig with a primary working and a secondary cooling-filtration circuit, connected via the oil tank. They then use ML on AWS to proactively predict the cooler condition, hydraulic accumulator condition, internal pump leakage condition, and valve condition.
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AIM313 - Build a Babel Fish with Machine Learning Language Services
In the novel, “The Hitchhiker's Guide to the Galaxy,” Douglas Adams described a Babel fish as a “small, yellow, and leech-like” device that you stick in your ear. In Star Trek, it is known simply as the universal language translator. Whatever you call it, there is no doubting the practical value of a device that is capable of translating any language into another. In this workshop, learn how to build a babel fish app that recognizes voice and converts it to text (speech-to-text), translates the text to a language of your choice, and converts translated text to synthesized speech (text-to-speech).
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AIM315-R1 - [REPEAT 1] Deep Learning for Edge to Cloud
In this workshop, you step into the role of a startup that has assumed the challenge of providing a new type of EDM music festival experience. Your goal is to use machine learning (ML) to develop a connected fan experience that enhances the festival. Come and get hands-on experience with Amazon SageMaker, AWS DeepLens, Amazon Rekognition, and AWS Lambda as you build and deploy an ML model and then run inference on it from edge devices.
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AIM316-R2 - [REPEAT 2] Get Started with Deep Learning and Computer Vision Using AWS DeepLens
If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Also learn to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application.
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AIM321 - Improve Your Customer Experience with Machine Translation
Machine Translation powers Amazon’s international expansion. Sign up to learn how you can leverage Amazon Translate to increase customer satisfaction, cut down response times, and build a more efficient customer support operation. For example, you can add real-time translation to chat, email, and helpdesk so an English-speaking agent can communicate with customers in their preferred language, or translate your knowledge base into multiple languages to make it accessible to customers and employees around the world.
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AIM333 - Unsupervised Learning with Amazon SageMaker
How do you use machine learning with data that isn't labeled? The unsupervised learning capabilities of Amazon SageMaker can easily handle unlabeled data. In this chalk talk, we discuss the intricacies of unsupervised algorithms that are built into Amazon SageMaker, including clustering with k-means and anomaly detection with Random Cut Forest.
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AIM334 - Build Models for Aerial Images Using Amazon SageMaker
There are unique challenges to building highly accurate models that detect small objects in aerial and overhead imagery. In this chalk talk, we dive deep into using convolutional neural networks (CNNs) with Amazon SageMaker in order to build and train aerial object detection models. We build advanced models using AWS public datasets, such as SpaceNet and LandSat, as we work with DigitalGlobe's GBDX Notebooks.
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AIM335 - Run XGBoost with Amazon SageMaker
XGBoost makes applying machine learning (ML) to real-world scenarios easy and powerful. Amazon SageMaker has XGBoost built in, and this enables the transition of ML models from training to production at scale. In this chalk talk, we discuss the details of using XGBoost on Amazon SageMaker, and we cover how to train and deploy ML models in a way that is simple, powerful, and scalable.
PowerPoint Presentation
AIM337 - Powering Multilingual Video Transcription, Translation, and Search
Automatic video transcription and translation can help make videos more available and accessible to a global audience in many languages, enabling your employees or customers to access, understand, and benefit from your content. In this chalk talk, we discuss how to transcribe videos, translate them in the required languages in a multilingual application, and enable video search in the viewer’s preferred language—all in an automated and cost-effective manner.
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AIM340 - Build an Intelligent Multi-Modal User Agent with Voice and NLU
Sophisticated AI capabilities can help us manage the exploding number of information sources and tools required to perform our daily tasks. In this chalk talk, we describe how intelligent agents can be designed to quickly and efficiently complete tasks delegated by users. To build this intelligent agent, we combine a number of AWS services, such as Amazon Polly, Amazon Lex, Amazon Rekognition, Amazon Sumerian, and Amazon ElastiCache along with other technologies, such as CLIPS and Reinforcement Learning. Come hear us discuss the project’s architecture, implementation, and demo progress made to date.
PowerPoint Presentation
AIM341 - Build a Visual Search Engine Using Amazon SageMaker and AWS Fargate
Visual search engines have a growing importance at companies like Pinterest as well as at e-commerce companies like Amazon.com and Gilt. In this chalk talk, we show you how to build a visual search engine using Amazon SageMaker and AWS Fargate.
PowerPoint Presentation
AIM342-R1 - [REPEAT 1] Create a Serverless Searchable Media Library
Companies have ever-growing media libraries, making them increasingly difficult to index and search. In this session, we describe how to maintain your library by using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend to perform automatic metadata extraction from image, video, and audio files. We show you how to then use this metadata to build a serverless media library that can be filtered by image tags, celebrities, and more.
PowerPoint Presentation
AIM343 - Build Automated Video Social Posts for Player Records and Highlights
In the world of sports entertainment, the fast pace of live events makes it difficult to keep up with new records and highlights that occur during games. In this session, learn how machine learning can combine internal statistics feeds with image player recognition to log when new player records are set. See how this solution uses Amazon Rekognition to identify the player, AWS Lambda to determine if the play is a new record for the particular athlete, and then automatically creates an image to share on social media that highlights the player in action.
PowerPoint Presentation
AIM347 - Detecting Financial Market Manipulation Using Machine Learning
Researchers from the University of Michigan and Georgia Tech, in collaboration with the AWS Research Initiative, have developed new techniques to identify financial market manipulation in high-volume, high-velocity market data streams. They are using a combination of data-driven and model-based techniques to identify financial market manipulation. In this session, we discuss the use of machine learning using Amazon SageMaker to study, process, and analyze huge volumes of data to prevent financial market manipulation.
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AIM348 - Translating Web Content Easily with Language Services from AWS
Providing multilingual content represents a great opportunity for site owners. Although English is the dominant language of the web, native English speakers comprise only 26% of the total online audience. In this chalk talk, we discuss how you can make your web content more accessible with text-to-speech and machine translation. By offering written and audio versions of your content in multiple languages, you can meet the needs of a larger international audience.
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AIM350 - Bring Your Own Apache MXNet and TensorFlow Scripts to Amazon SageMaker
Amazon SageMaker enables you to bring your existing Apache MXNet or TensorFlow script for your machine learning models. In this session, we walk through the details of bringing your own script for training your models at scale. We also go into detail on using local containers for repeated experiments for ease of use and scalability.
PowerPoint Presentation
AIM354 - Build Human-in-the-Loop Systems with AWS Lambda and Mechanical Turk
Building human-in-the-loop solutions can be very effective, but integrating humans into existing ML or business process workflows can be complex. Learn how you can easily connect the Amazon Mechanical Turk (Mechanical Turk) on-demand human intelligence platform with other AWS services, such as Amazon S3, Amazon Lex, Amazon Polly, and Amazon Rekognition with AWS Lambda.
PowerPoint Presentation
AIM385 - Build an Event Registration Kiosk Powered by Facial Recognition
Registering for an event and waiting in line to verify your ticket in order to enter is a difficult process. Machine learning provides a solution to this challenge by using facial recognition to streamline the event registration process. This minimizes lines and enables attendees to quickly register and enter an event. In this session, we share best practices for building an event registration kiosk powered by facial recognition, integrating it with third-party registration services, and creating a web-based kiosk application.
PowerPoint Presentation
AIM390 - Machine Learning Your Eight-Year-Old Would Be Proud Of
Come see examples of how Bebo uses Amazon SageMaker to power massive Fortnite tournaments every week. Traditional sports require referees, scorekeepers, field staff, and broadcast crews for every match. But esports are digital by nature. In this session, learn how machine learning and computer vision are enabling esports to occur at a massive scale. Learn how Bebo developed a model that can detect every victory and elimination, and can even prevent cheating on their tournament platform.
PowerPoint Presentation
AWS re:Invent 2018: Machine Learning Your Eight-Year-Old Would Be Proud Of (AIM390)
AIM404-R - [REPEAT] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. 21st Century Fox
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples.
PowerPoint Presentation
AWS re:Invent 2018: Build & Deploy ML Models Quickly & Easily with Amazon SageMaker (AIM404-R)
AIM404-R1 - [REPEAT 1] Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ft. the NFL
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples.
PowerPoint Presentation
AIM406 - Unlock the Full Potential of Your Media Assets, ft. Fox Entertainment Group
Machine learning (ML) enables developers to build scalable solutions that maximizes the use of media assets through automatic metadata extraction. From automatic transcription and language translation to face detection and celebrity recognition, ML enables you to automate manual workflows and optimize the use of your video content. In this session, learn how to use services such as Amazon Rekognition, Amazon Translate, and Amazon Comprehend to build a searchable video library, automate the creation of highlight reels, and more.
PowerPoint Presentation
AIM407-R - [REPEAT] Build Deep Learning Applications Using Apache MXNet, Featuring Workday
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, and natural language processing at scale. In this session, learn how to get started with MXNet on the Amazon SageMaker machine learning platform. Hear from Workday about how they built computer vision and natural language processing (NLP) models using MXNet to automatically extract information from paper documents, such as expense receipts and populate data records. Workday also shares its experience using Sockeye, an MXNet toolkit for quickly prototyping sequence-to-sequence NLP models.
PowerPoint Presentation
AIM415-R1 - [REPEAT 1] Capture Voice of Customer Insights with NLP & Analytics
Understanding your customers is easier today than ever before. Natural language capabilities can capture a wealth of information, such as user sentiment and conversational intent. This workshop teaches you how to build an analytics pipeline that includes natural language processing (NLP) to better understand how to improve the customer experience. Attendees learn how to use AWS services, including Amazon Comprehend and Amazon Transcribe, to process and perform analysis on customer data, such as contact center call recordings.
PowerPoint Presentation
AIM416 - Build an ETL Pipeline to Analyze Customer Data
Consumers today freely express their satisfaction or frustration with a company or product online through social media, blogs, and review platforms. Sentiment analysis can help companies better understand their customers' opinions and needs, and make more informed business decisions. In this workshop, learn how to use Amazon Comprehend to analyze sentiment. Also learn how to build a serverless data processing pipeline that consumes raw Amazon product reviews from Amazon S3, cleans the dataset, extracts sentiment from each review, and writes the output back to Amazon S3.
PowerPoint Presentation
AIM418 - Build Deep Learning Applications Using MXNet and Amazon SageMaker
In this workshop, learn how to get started with the Apache MXNet deep learning framework using Amazon SageMaker, a fully managed platform to build, train, and deploy machine learning models at scale quickly and easily. Learn how to build a model using MXNet for a computer vision use case. Once the model is built, learn how to quickly train it to get the best possible results and then easily deploy it to production using Amazon SageMaker.
PowerPoint Presentation
AIM419 - Train Models on Amazon SageMaker Using Data Not from Amazon S3
Questions often arise about training machine learning models using Amazon SageMaker with data from sources other than Amazon S3. In this chalk talk, we dive deep into training models in real time using data from Amazon DynamoDB or a relational database. We demonstrate how training models with Amazon SageMaker is quick and easy, regardless of the data source.
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AIM431 - Deep Learning-Based Text-to-Speech Synthesis with MXNet
Text-to-speech (TTS) is used in many applications, such as artificial assistants, readers for ebooks, character voices for games, and more. In this session, learn how to build TTS systems with deep learning techniques for multiple voices using the Gluon interface, an open source library in Apache MXNet.
PowerPoint Presentation
ALX201-R - [REPEAT] Alexa Everywhere: A Year in Review
Since its launch in 2015, Alexa has enabled new experiences across many device form factors at home, work, in the car, and on the go. With over 50,000 published skills, hundreds of new API features releases, and numerous Alexa-enabled devices, it can be hard to keep track with of the current pace. In this session, we get you up to speed on the current Voice First movement, the current Conversational AI trends, and we give demonstrations of some of the latest Alexa features and devices. Come learn about the new Alexa Skills Kit (ASK) multi-modal framework, Alexa Presentation Language (APL) for developers, Alexa skill fulfillment and consumables for customers, and some of the latest device offerings utilizing the Alexa Voice Service (AVS) and the new Alexa Gadgets Toolkit.
AWS re:Invent 2018: Alexa Everywhere: A Year in Review (ALX201-R)
PowerPoint Presentation
ALX304 - Tailor Your Alexa Skill Responses to Deliver Truly Personal Experiences
Delivering truly personal responses to customers is one of the most engaging features of an Alexa skill. In this session, learn the different approaches and best practices in creating responses that are tailored to each one of your customers. By applying what you learn, you can keep them coming back to your voice experience.
AWS re:invent 2018 機械学習関連セッションのプレゼン資料・動画一覧 #reinvent
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