MLflow patterns for taking models to production.
What it does
Guides the creation of MLflow-based workflows for tracking, validating, deploying, and monitoring machine learning models.
When to use it
Use when building machine learning workflows spanning experimentation, production deployment, and monitoring.
How to use it
It changes how the agent implements machine learning lifecycle workflows by providing MLflow patterns and code examples.
What you provide
- An existing project
Uses
Latest review
What you need · 3
Requires Python 3.8 or later.
Lists MLflow 2.0 or later as a prerequisite.
Lists scikit-learn as a prerequisite for the model-training workflows.
Access · 1
This skill
MLflow
Write
Logs prediction data to MLflow