By Vitale Sparacello, Murilo Cunha, Bram Vandendriessche

"MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently." - wiki

At datartoots we've been pioneers of the MLOps methodology since the very beginning. For us MLOps means being able to identify all the business challenges, deliver the best solution quickly and efficiently, and monitor the project's evolution over time.

To promote MLOps best practices we have run a workshop to KU Leuven university students. In this repository you can find the three different notebooks (a tutorial, an exercise and exercise solutions) which we created for the MLOps workshop. The tutorial gives a gentle introduction to three popular frameworks for MLOps: DVC, MLflow and Pycaret. It will show you how to version your data using DVC, track your experiments using MLFlow and doing autoML using Pycaret. Afterwards you can practice your newly acquired knowledge by completing the exercise notebook.

The code

GitHub - datarootsio/tutorial-mlops: MLOps exercise material.
MLOps exercise material. Contribute to datarootsio/tutorial-mlops development by creating an account on GitHub.

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