Architect Your ML Project

Architect Your ML Project

Steps in ML Project

Steps in 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

Project Structure

MLExpert is loading...



  1. Poetry (opens in a new tab)

  2. DVC (opens in a new tab)

  3. DVC Remotes (opens in a new tab)

  4. Run experiments with DVC (opens in a new tab)