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.
https://2022.pycon.de
More details at the conference page: https://2022.pycon.de/program/DV8PJT
Twitter: https://twitter.com/pydataberlin
Twitter: https://twitter.com/pyconde
