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Managing Machine Learning Models with MLflow

Learn to use MLflow to track and package a machine learning model, and see the process for getting models into production.
Weston Bassler
Weston Bassler

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Code Along ↗

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.

Key Takeaways:

  • Learn how to get started with MLflow.
  • Learn the steps needed to make a reproducible model.
  • Learn about model tracking and packaging.

Resources

Topics

Data EngineeringMachine LearningPythonData Scientist

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