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Machine Learning in Production

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Learn best practices for managing machine learning experiments and models with MLflow. This course will teach you how to: 

  • Use MLflow to track the machine learning lifecycle, package models for deployment and manage model versions 
  • Handle various production issues, deployment paradigms and post-production concerns

By the end of this course, you will have built an end-to-end pipeline to log, deploy and monitor machine learning models.

This course will prepare you to take the Databricks Certified Machine Learning Professional exam.


1 full day or 2 half days


  • Track, version and manage machine learning experiments
  • Leverage Databricks Feature Store for reproducible data management
  • Implement strategies for deploying models for batch, streaming and real time
  • Build monitoring solutions, including drift detection


  • Intermediate experience with Python and pandas
  • Working knowledge of machine learning and data science (including technologies such as scikit-learn and TensorFlow)
  • Familiarity with Apache Spark


Day 1

  • ML in Production Vverview
  • Data Management with Delta and Databricks Feature Store
  • Experiment Tracking and Versioning with MLflow Tracking
  • Model Management with MLflow Models and Model Registry
  • Automated Testing with webhooks
  • Deployment Paradigms
  • Monitoring and CI/CD