Rootsacademy: MLOps in practice

MLflow - Workshop

MLflow is an open source platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, deployment, and tracking. This repo's main content is mlflow_tutorial. These directories contain exercises to show following capabilities of the package.

1. Experiment tracking
2. Model registry
3. Deployment

Getting started

Install the requirements and then move on to the notebooks.

if you work with virtual environments:

python3.11 -m venv mlflow-env  
source mlflow-env/bin/activate

and then:

pip install -r requirements.txt
jupyter notebook

Have fun!