Try one of these tutorials to get started. You can import these notebooks to your Databricks workspace.
| Tutorial | Description |
|---|---|
| Classic ML | End-to-end example of training a classic ML model in Databricks. |
| scikit-learn | Use one of the most popular Python libraries for machine learning to train machine learning models. |
| MLlib | Examples of how to use the Apache Spark machine learning library. |
| Deep learning using PyTorch | End-to-end example of training a deep learning model in Databricks using PyTorch. |
| TensorFlow | TensorFlow is an open-source framework that supports deep-learning and numerical computations on CPUs, GPUs, and clusters of GPUs. |
| Mosaic AI Model Serving | Deploy and query a classic ML model using Mosaic AI Model Serving. |
| Foundation model APIs | Foundation model APIs provide access to popular foundation models from endpoints that are available directly from the Databricks workspace. |
| Agent framework quickstart | Use Mosaic AI Agent Framework to build an agent, add a tool to the agent, and deploy the agent to a Databricks model serving endpoint. |
| Trace a GenAI app | Trace an app's execution flow with visibility into every step. |
| Evaluate a GenAI app | Use MLflow 3 to create, trace, and evaluate a GenAI app. |
| Human feedback quickstart | Collect end-user feedback and use that feedback to evaluate your GenAI app's quality. |
| Build, evaluate, and deploy a retrieval agent | Build an AI agent that combines retrieval with tools. |
| Query OpenAI models | Create an external model endpoint to query OpenAI models. |