Pyfunc custom schema driver notebook
This notebook shows you how to log, register, and deploy a Python AI agent compatible with Mosaic AI Agent Framework that accepts custom inputs and returns custom outputs.
To ensure compatibility, the agent must conform to Mosaic AI Agent Framework schema requirements, see (AWS | Azure).
Model-as-code notebook
Mosaic AI Agent Framework uses MLflows Models-as-code development workflow, which requires two notebooks:
- A driver notebook that logs, registers, and deploys the agent (this notebook)
- An agent notebook that defines the agent's logic
For more information on Model-as-code, see MLflow's Models as code guide.
Requirements
This notebook requires a workspace that has been enabled for Unity Catalog.
2
Log the agent as an MLflow model
4
Register the model to Unity Catalog
Update the catalog
, schema
, and model_name
below to register the MLflow model to Unity Catalog.
6
Deploy the agent
8