In this course, you will learn the best practices for managing machine learning experiments and models with MLflow. There are two main components in this course: (i) using MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions (ii) examining various production issues, different 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 (scikit-learn, TensorFlow, etc.)
- Familiarity 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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