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Introduction to MLOps with MLflow


Speaker:: Tobias Sterbak

Track: General: Production
Machine learning requires experimenting with different datasets, data preparation steps, and algorithms to build a model that maximizes some target metric. Once you have built a model, you also need to deploy it to a production system, monitor its performance, and continuously retrain it on new data and compare with alternative models. A possible solution to managing parts of this complexity is offered by **MLFlow**.

In this tutorial, you will learn how to use MLflow to:

- _Set up_ a tracking server and a model repository.
- _Keep track_ of machine learning training and experiment results (parameters, metrics and artifacts) with **MLflow Tracking**.
- _Package_ the training code in a reusable and reproducible format with **MLFlow Projects**.
-  _Deploy_ the model into a HTTP server with **MLFlow Models** and keep track of it's state.

Recorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.
More details at the conference page:


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