Note
Access to this page requires authorization. You can try signing in or changing directories.
Access to this page requires authorization. You can try changing directories.
This page provides an overview of tools for building, deploying, and managing generative AI (gen AI) apps on Azure Databricks.
| Page | Description |
|---|---|
| Get started: no-code GenAI | Try AI Playground for UI-based testing and prototyping. |
| Get started: MLflow 3 for GenAI | Try MLflow for GenAI tracing, evaluation, and human feedback. |
Serve and query gen AI large language models (LLMs)
Serve a curated set of gen AI models from LLM providers such as OpenAI and Anthropic and make them available through secure, scalable APIs.
| Feature | Description |
|---|---|
| Foundation Models | Serve gen AI models, including open source and third-party models such as Meta Llama, Anthropic Claude, OpenAI GPT, and more. |
Build and deploy enterprise-grade AI agents
Build and deploy your own agents, including tool-calling agents, retrieval-augmented generation apps, and multi-agent systems.
| Feature | Description |
|---|---|
| AI Playground (no code) | Prototype and test AI agents in a no-code environment. Quickly experiment with agent behaviors and tool integrations before generating code for deployment. |
| Agent Bricks | Build and optimize domain-specific AI agent systems with a simple interface. Focus on your data and metrics while Agent Bricks streamlines implementation. |
| Build custom agents | Author, deploy, and evaluate agents using Python. Supports agents written with any authoring library. Integrated with MLflow Tracing. Iterate quickly using Databricks Apps. |
| AI agent tools | Create agent tools to query structured and unstructured data, run code, or connect to external service APIs. |
| MCP (Model Context Protocol) | Standardize how agents connect to data and tools with a secure, consistent interface. |
Evaluate, debug, and optimize agents
Track agent performance, collect feedback, and drive quality improvements with evaluation and tracing tools.
| Feature | Description |
|---|---|
| MLflow Tracing | Use MLflow Tracing for end-to-end observability. Log every step your agent takes to debug, monitor, and audit agent behavior in development and production. |
| Agent Evaluation | Use Agent Evaluation and MLflow to measure quality, cost, and latency. Collect feedback from stakeholders and subject matter experts through built-in review apps and use LLM judges to identify and resolve quality issues. |
| Monitor agents | Use the same evaluation configuration (LLM judges and custom metrics) in offline evaluation and online monitoring. |