Machine Learning Operations - Hands-on workshop

In the workshop, we take a simple machine learning project from a Jupyter Notebook into an automated ML training pipeline. We start by adding experiment tracking to the notebook using MLflow. Then, we refactor the project into a pipeline of data assets using dagster.

• Step 1: Experiment tracking
• Step 2: Data orchestration
• Step 3: IO management
• Step 4: Configuration
• Step 5: Automation and scheduling

This repository provides the code skeleton that will be used to implement the project.