Building Single-Agent Applications on Databricks
This course provides hands-on training for building single-agent applications on the Databricks Data Intelligence Platform. Students will learn to create AI agents that leverage Unity Catalog functions as tools, implement comprehensive tracing and monitoring with MLflow, and deploy agents using both traditional frameworks like LangChain and modern solutions like Agent Bricks. The course covers the complete agent lifecycle from initial tool creation and testing in AI Playground through production deployment with governance, evaluation, and continuous improvement capabilities.
Note: This is the second course in the 'Generative AI Engineering with Databricks’ series. It was previously named 'Generative AI Application Development'.
The content was developed for participants with these skills/knowledge/abilities:
• Python-Specific Prerequisites
Learners must be comfortable writing production-quality Python, not scripts.
- Core Python syntax and data structures
- Functions, classes, and basic OOP patterns
- Exception handling and error propagation
- Decorators
- Type hints and docstrings
• SQL-Specific Prerequisites
Learners must be able to define reusable SQL logic, not just query tables.
- Writing SELECT queries with filters and aggregations
- Understanding SQL data types and NULL handling
- Creating parameterized SQL functions
- Using CREATE OR REPLACE FUNCTION syntax
- Writing clear SQL comments for documentation
• Databricks-Specific Prerequisites
Learners must be comfortable operating inside the Databricks platform.
- Navigating the Databricks workspace and notebooks
- Running notebook cells and interpreting outputs
- Understanding basic compute concepts (especially serverless)
- Using Catalog Explorer to inspect registered assets
- Awareness of Databricks-managed services (Model Serving, AI Playground)
• GenAI / Agent-Specific Prerequisites
Learners must understand how LLM-powered agents behave, even if frameworks are taught in-course.
- What Large Language Models are and what they can and cannot do
- Basic prompt engineering concepts
- High-level understanding of Retrieval-Augmented Generation (RAG)
- Conceptual understanding of agent reasoning and tool invocation
- Familiarity with REST APIs and JSON payloads
• Optional but Helpful (Not Required)
- MLflow fundamentals (tracking, model registry, tracing)
- Agent frameworks (e.g., LangChain)
• Databricks-related recommended training: AI Agents Fundamentals, Get Started with AI Agents
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