this /static/media/twitter/DV8PJT.png

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.

Please make sure to follow the instructions on to setup your python environment before attending! That way everything will run smoothly :)

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.

Tobias Sterbak

Tobias Sterbak is a Data Scientist and Software Developer from Berlin. He has been working as a freelancer in the field of Natural Language Processing since 2018. On the blog he occasionally writes about topics in Machine Learning and Natural Language Processing. In his private life he is interested in data privacy, open source software, remote work and dogs.

visit the speaker at: GithubHomepage