James Lee

This hands-on course introduces how to build single-agent, multi-agent, and multi-model systems on Agent Bricks, including key components, architectures, and use cases. You'll learn how to create agent tools using Unity Catalog functions to extend agent capabilities. You'll begin by using Genie to build agents that can interact with structured data, before extending these concepts to create a second agent designed for working with unstructured data and external resources. Finally, you'll explore how Agent Bricks provides a declarative approach to building multi-agent applications, offering a streamlined alternative to manual development. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026. Prerequisites - Familiarity with the Databricks Data Intelligence Platform, including workspaces and notebooks - Intermediate Python and SQL proficiency - Familiarity with Generative AI fundamentals, including LLMs and prompting strategies - Familiarity with Retrieval Augmented Generation (RAG) architecture - Familiarity with API usage and function calling for LLM interactions with external tools and services

This course teaches you how to design, build, and maintain a Databricks Genie Space, a natural language interface that enables business users to ask questions about governed data and receive SQL-backed answers without writing code. You will learn how Genie fits into the Databricks AI/BI product family and how it translates natural language into reliable SQL queries. The course focuses on what it takes to create a Genie Space that delivers accurate, consistent, and trustworthy results. You will follow a complete end-to-end workflow, from understanding source data and defining benchmarks to configuring and refining a Genie Space using the full set of Knowledge Store curation tools. These include metadata, synonyms, prompt matching, SQL logic, example queries, and text instructions. You will also learn how to share Genie Spaces, understand how Unity Catalog governance is automatically enforced, and use monitoring and user feedback to continuously improve quality over time. By the end of the course, you will be able to create and manage a production-ready Genie Space that delivers governed, self-service conversational analytics at scale. Prerequisites General familiarity with the…

This hands-on course equips learners with hands-on expertise to implement robust, enterprise-scale data governance in Databricks using Unity Catalog. Implement access control at scale with ABAC, enable comprehensive observability and auditing, monitor and assure data quality, visualize and manage complete data and model lineage, and effectively track usage, costs, and data quality across your platform. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026. Prerequisites - Familiarity with the Databricks Data Intelligence Platform, including workspaces, notebooks, and basic cloud concepts - Intermediate SQL experience, including creating and managing tables, views, and functions, and granting/revoking privileges - Working knowledge of data governance concepts, including access control, security policies, and fine-grained techniques such as row-level filters and column masking - Understanding of Unity Catalog's object model (metastore, catalogs, schemas, tables, volumes, functions, models) and basic data lineage principles - Basic awareness of metadata management and data discovery practices