MLflow

MLflow is an open-source platform for managing the full machine learning (ML) and generative AI lifecycle, used by data scientists and AI engineers to track experiments, package models, manage model versions, evaluate and monitor LLM applications and agents, and deploy to production.

MLflow’s classic ML workflow centers on these components:

• Tracking logs parameters, metrics, and artifacts for each run
• Models packages trained models in a library-agnostic format
• Model Registry handles centralized versioning
• Projects packages code in reproducible run environments

MLflow 3 added Logged Models, which track a model as a persistent object across runs. MLflow also supports LLM workflows through MLflow Tracing for observability, LLM-as-a-judge evaluation, a Prompt Registry for versioning and reusing prompt templates, and an AI Gateway that fronts multiple model providers behind one endpoint, plus built-in integrations with LangChain, LlamaIndex, OpenAI, DSPy, AutoGen, and Pydantic AI.

Official website: mlflow.org

Related Resources

Tutorial

Setting Up Python for Machine Learning on Windows

In this step-by-step tutorial, you’ll cover the basics of setting up a Python numerical computation environment for machine learning on a Windows machine using the Anaconda Python distribution.

basics data-science machine-learning

For additional information on related topics, take a look at the following resources:

• LlamaIndex in Python: A RAG Guide With Examples (Tutorial)
• Build an LLM RAG Chatbot With LangChain (Tutorial)
• Stochastic Gradient Descent Algorithm With Python and NumPy (Tutorial)
• Split Your Dataset With scikit-learn's train_test_split() (Tutorial)
• Using LlamaIndex for RAG in Python (Course)
• LlamaIndex in Python: A RAG Guide With Examples (Quiz)
• First Steps With LangChain (Course)
• Build an LLM RAG Chatbot With LangChain (Quiz)
• Splitting Datasets With scikit-learn and train_test_split() (Course)
• Split Your Dataset With scikit-learn's train_test_split() (Quiz)