AI and machine learning tutorials

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.