In this course, you will learn MLOps best practices for putting machine learning models into production. The first half of the course uses a feature store to register training data and uses MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions. The second half of the course examines production issues including deployment paradigms, monitoring, and CI/CD. 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 (or completion of Introduction to Python for Data Science & Data Engineering)
- Familiarity with Apache Spark (or completion of Apache Spark Programming)
- Working knowledge of machine learning and data science (or completion of Scalable Machine Learning with Apache Spark)
- ML in production overview
- 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
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