Architect Your ML Project
The ability to craft robust, scalable, and reproducible ML pipelines stands as a cornerstone skill for practitioners. As projects grow in complexity, managing dependencies, ensuring repeatability, and deploying models efficiently become critical challenges. This tutorial is designed to guide you through the process of architecting an ML project pipeline that adheres to industry best practices and leverages cutting-edge tools.
We'll be working on a real-world machine learning project focused on predicting body fat percentage based on personal measurements.
Tutorial Goals
In this tutorial you will:
- Learn how to organize a real-world machine learning project
- Use Poetry for managing project dependencies
- Apply DVC to track and version data and model artifacts
- Build a REST API with FastAPI to serve your model predictions