Project Idea: Automated Model Training and Deployment Pipeline

[PRIYANSHU2026]

Project Idea: Automated Model Training and Deployment Pipeline

Project Description: Develop an automated end-to-end machine learning pipeline using Kubeflow Pipelines to streamline the process of model training, validation, and deployment. The pipeline will take raw data, preprocess it, train multiple models, validate their performance, and deploy the best model to a production environment. This project aims to simplify and automate the repetitive tasks involved in the machine learning lifecycle, making it easier to manage experiments and deploy models efficiently.

Key Features:

1. Data Ingestion and Preprocessing: Create pipeline components to ingest raw data from various sources and preprocess it (e.g., data cleaning, normalization, feature engineering).
2. Model Training: Implement components to train multiple models (e.g., logistic regression, decision trees, neural networks) using the preprocessed data.
3. Model Validation: Add components to validate the trained models using techniques like cross-validation, and select the best model based on performance metrics.
4. Model Deployment: Develop a component to deploy the selected model to a production environment, such as a Kubernetes cluster, using a REST API endpoint.
5. Experiment Tracking: Use Kubeflow Pipelines' metadata tracking capabilities to log and visualize experiment results, including model parameters, metrics, and artifacts.
6. CI/CD Integration: Incorporate CI/CD practices by integrating with tools like GitHub Actions or Jenkins to automate the pipeline execution upon code or data changes.

Explanation:

1. Data Ingestion: A component to load raw data.
2. Data Preprocessing: A component to preprocess the data.
3. Model Training: A component to train a logistic regression model.
4. Model Validation: A component to validate the trained model's accuracy.
5. Model Deployment: A component to save and deploy the trained model.

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