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

MLflow organizes its features into tools you can use for traditional ML and modern gen AI:

Features for traditional ML and deep learning

• Model tracking: An automated log that records code versions, parameters, performance metrics, and charts every time you train a model during testing and research. 
• Model packaging: A standardized packaging format that groups a model along with all the software dependencies it needs to run predictably in any environment. 
• MLflow Model Registry: A central hub for keeping track of approvals and transitions as the project reaches a more mature stage. The Model Registry becomes a source of truth and serves as a link back to the original experiment (in model tracking) for auditability.
• Model deployment: A set of serving tools that convert saved models into web-friendly services (REST APIs), containers, or inference pipelines. 
• Model evaluation: A framework that automatically calculates accuracy metrics and generates performance charts for trained models. 

Features for generative and agentic AI

• MLflow Tracing: An OpenTelemetry-compatible observability tool that visualizes the “thought process” of AI agents by logging inputs, outputs, tool choices, and token costs. 
• LLM evaluation: Also referred to as “LLM-as-a-Judge,” this feature uses secondary language models to check the quality of answers coming out of your project. This can test for bias and keep guardrails on your model output. 
• MLflow Prompt Registry: A dedicated workspace to track, manage, and organize prompt templates. 
• MLflow AI Gateway: A central hub that tracks and routes data as it moves through API keys and external providers. It helps manage budget and enforce rate limits. 
• MLflow MCP Server: A connection point that lets AI applications and coding assistants interact with external data sources and tools using Model Context Protocol (MCP). 

MLflow is designed to be framework neutral, meaning it works with almost any tool a developer or data scientist would want to use. Popular frameworks like PyTorch, LangChain, Docker, and many more can be integrated throughout the MLflow lifecycle. 

See how AI observability works with MLflow

MLflow makes life easier for developers and data scientists, but it also appeals to IT leaders creating enterprise AI solutions. MLflow’s behind-the-scenes governance helps protect user privacy, catch errors before release, and maintain dependable uptime. This gives IT decision makers control over their AI infrastructure across several critical areas: 

• Digital sovereignty: Enterprises can host MLflow entirely on premise, inside a private cloud network, or in an air-gapped system. Sensitive data can stay within an organization’s security parameter rather than being exported through a third-party vendor. 
• Regulatory compliance: Some industries (like finance, healthcare, and legal) need to account for every step of an automated decision. MLflow captures this information and provides complete audit trails and lineage metadata, giving compliance teams full visibility into how outputs were generated. This transparency can help with explainable AI and sovereign AI efforts. 
• Centralized cost control: Through the AI gateway, IT teams can route all external AI traffic (everything flowing between your agents, language models, and external tools) through a single checkpoint. This lets organizations set budget limits, manage API keys and MCP connections, and track expenses across teams.

Red Hat® AI is built for fast, flexible, and efficient inference through its vLLM-powered server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our products give you full control of AI workflows from end to end at any scale. 

The Red Hat AI portfolio includes Red Hat AI Enterprise, a platform for deploying, managing, and scaling AI inference, agentic AI workflows, and AI-powered applications on any infrastructure.

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