Advanced Machine Learning Operations

This course offers a practical introduction to the Mosaic AI platform, focusing on its key components and features for building and deploying generative AI systems. Participants will learn how Databricks facilitates the development of scalable generative AI solutions and explore Mosaic AI tools such as Vector Search, the Agent Framework, and MLflow’s generative AI capabilities for model tracking and logging. This course includes hands-on experience in constructing and evaluating Retrieval-Augmented Generation (RAG) pipelines, deploying generative AI agents, and leveraging evaluation frameworks to optimize performance. By the end of the course, learners will be equipped with the skills to design, deploy, and monitor common generative AI applications using Mosaic AI.

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.

Note: This is the second course in the 'Machine Learning with Databricks’ series.

In this course, you will learn basic skills that will allow you to use the Databricks Data Intelligence Platform to perform a simple data engineering workflow and support data warehousing endeavors. You will be given a tour of the workspace and be shown how to work with objects in Databricks such as catalogs, schemas, volumes, tables, compute clusters, and notebooks. You will then follow a basic data engineering workflow to perform tasks such as creating and working with tables, ingesting data into Delta Lake, transforming data through the medallion architecture, and using Databricks Workflows to orchestrate data engineering tasks. You’ll also learn how Databricks supports data warehousing needs through the use of Databricks SQL, Delta Live Tables, and Unity Catalog. With the purchase of a Databricks Labs subscription, the course also closes out with a comprehensive lab exercise to practice what you’ve learned in a live Databricks Workspace environment.