Iván Palomares Carrascosa writes about using the Scikit-LLM library alongside MLflow to build, track, compare, and register scikit-learn pipelines that incorporate large language models. The article provides a workflow for ensuring model versioning and reproducibility by logging different LLM backends as environment parameters and promoting successful pipeline versions into an MLflow Model Registry.
- Uses the `scikit-llm gpt4all » ` installation option to ensure compatibility with local execution.
- Demonstrates how to use `cloudpickle` for serialization when working with scikit-learn models in MLflow.
- Highlights a two-step workflow of logging experiments first and then registering only "winner" models to keep the registry clean.
- Uses the `scikit-llm gpt4all » ` installation option to ensure compatibility with local execution.
- Demonstrates how to use `cloudpickle` for serialization when working with scikit-learn models in MLflow.
- Highlights a two-step workflow of logging experiments first and then registering only "winner" models to keep the registry clean.
An article detailing how to build a flexible, explainable, and algorithm-agnostic ML pipeline with MLflow, focusing on preprocessing, model training, and SHAP-based explanations.
Learn how to build an efficient pipeline with Hydra and MLflow
This article provides an introduction to Mlflow, an open-source platform for end-to-end machine learning lifecycle management. The article focuses on using MLflow as an orchestrator for machine learning pipelines, explaining the importance of managing complex pipelines in machine learning projects.
