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