Deploying and Monitoring Agent Applications on Databricks
This course covers the end-to-end lifecycle for deploying and monitoring generative AI agents on Databricks. Participants will learn how to deploy agents as Databricks Apps using Declarative Automation Bundles (DABs), integrate tools via the Model Context Protocol (MCP), instrument agents with MLflow Tracing, and evaluate production quality using scorers, multi-turn judges, and online evaluation. Through hands-on demos and labs, participants will gain practical experience building, observing, and monitoring production-grade AI agents on the Databricks platform.
Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.
In this course, the content was developed for participants with these skills/knowledge/abilities:
• Familiarity with Databricks workspace and notebooks
• Familiarity with Unity Catalog
• Experience building agents using the OpenAI Agents SDK
• Basic knowledge of MLflow and Python
• Familiarity with GenAI agent concepts (LLM calls, tool invocation, retrieval)
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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