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Managing Machine Learning Models with MLflow

Key Takeaways:
  • Learn how to get started with MLflow.
  • Learn the steps needed to make a reproducible model.
  • Learn about model tracking and packaging.
Tuesday, October 17 11AM ET
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Machine Learning applications are complex and can be difficult to track, hard to reproduce, and problematic to deploy. MLflow is designed to simplify the challenges of managing the machine learning lifecycle.

In this code-along you'll learn to use MLflow to track and package a machine learning model, and see the process for getting models into production. Throughout the code-along, you’ll learn how to get started with MLflow, how to make a reproducible machine learning model, and how to get started with model tracking and packaging.

Presenter Bio

Weston Bassler Headshot
Weston BasslerSenior Machine Learning Engineer at Emburse

Weston develops pipelines and tools to help his team's machine learning models scale, and for his data science team to work more efficiently. He has a background in systems engineering and infrastructure, and has worked at a variety of startups and blue-chip companies. Weston is the instructor of DataCamp's Introduction to MLflow course.

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