In this article, you will learn how to build, track, compare, and register scikit-learn pipelines that integrate large language models using Scikit-LLM and MLflow.
Topics we will cover include:
How to configure Scikit-LLM and MLflow to support local large language model execution and experiment tracking.
How to log multiple pipeline versions across different large language model backends and compare them using MLflow’s tracking API.
How to promote the best-performing pipeline from a tracked experiment into MLflow’s Model Registry for deployment.
