What is MLflow?

MLflow is a tool that helps developers keep machine learning (ML) and AI projects organized from early experimentation to live deployment. It provides a unified platform to track, audit, and manage the complete lifecycle of everything from traditional predictive models to modern AI agents. 

As an open source, vendor-neutral tool, MLflow runs anywhere (locally or with your cloud provider of choice) and integrates with most popular ML frameworks. This flexibility has made MLflow popular with individual developers and enterprises alike.

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Before the rise of generative AI (gen AI), machine learning focused on predictive tasks like forecasting, flagging, and sorting data. Even then, data science teams lacked standard tools to track experiments and deploy models. Instead, model iterations were tracked across spreadsheets, personal notes, and disconnected scripts. 

Big technology companies had access to internal proprietary systems, but an open source option didn’t exist. As a result, many projects failed to move successfully from an experimental phase to a live operational phase. 

Databricks created MLflow as a solution to this problem and later donated it to the Linux® Foundation. This gave the technology industry a collection of open source tools to support each phase of the ML lifecycle:

• Testing phase: MLflow replaces scattered manual notes and supplies a tracking and prompt registry to log every parameter change, code version, and update. For gen AI applications, teams can test different prompts from a prompt registry. This lets them run an automated evaluation (or LLM-as-a-Judge) to score generated outputs for accuracy and hallucination risk before going live. 
• Handoff phase: MLflow replaces manual handoffs and packages code into a standardized container that runs the same way in any environment. The model registry functions as a definitive record for version histories, and approvals as assets move from testing to production.
• Launch phase: Once a model is approved in the registry, MLflow converts saved assets into representational state transfer application programming interfaces (REST APIs) or standard containers ready for faster integration into applications or servers. For gen AI applications, the AI gateway acts as a traffic controller, managing API keys and routing data. 
• Monitoring phase: MLflow offers tracing and evaluation tools to measure live outputs for accuracy and inspect the multistep reasoning of agents.

Watch a demo: Agentic tracing and observability with MLflow