Data Governance with Unity Catalog

This comprehensive course provides a practical guide to developing traditional machine learning models on Databricks, emphasizing hands-on demonstrations and workflows using popular ML libraries. This course focuses on executing common tasks efficiently with AutoML and MLflow. Participants will delve into key topics, including regression and classification models, harnessing Databricks' capabilities to track model training, leveraging feature stores for model development, and implementing hyperparameter tuning. Additionally, the course covers AutoML for rapid and low-code model training, ensuring that participants gain practical, real-world skills for streamlined and effective machine learning model development in the Databricks environment.

This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference.

This course will guide participants through an exploration of machine learning operations on Databricks. This will begin with an introduction to modern machine learning operations as a combination of DevOps, DataOps, and ModelOps, including an overview of each component. Next, the course will cover the Databricks-recommended architectures and solutions for machine learning operations. This will focus on capabilities like MLOps Stacks, MLflow, Model Serving, Unity Catalog, and Lakehouse Monitoring. Finally, the final section of the course will be a practical walkthrough of a simple machine learning operations project using these capabilities, including: an initial development phase, a staging phase, and a production stage. By the end of this course, learners will be familiar with the basic end-to-end machine learning operations workflow on Databricks.