MLflow

• Experiment tracking with automatic logging of parameters, metrics, and artifacts
• Model registry for versioning, managing, and organizing machine learning models
• Easy deployment of models to cloud, local, or production environments
• Centralized model lifecycle management from development to production
• Integration with popular ML frameworks like TensorFlow, PyTorch, and Scikit-learn
• Visualization tools for comparing experiment performance and results
• Reproducibility support through environment and dependency tracking
• Collaboration features for teams working on shared ML projects
• Observability and tracing for monitoring AI and generative AI workflows
• Support for multi-language development including Python, R, Java, and REST APIs

MLflow is an open-source AI engineering platform designed to help teams build, evaluate, deploy, and operate machine learning models, LLM applications, and AI agents. Originally created at Databricks to address challenges around experimentation, reproducibility, and model deployment, MLflow has expanded into a broader platform for modern AI engineering and production operations.

For traditional machine learning workflows, MLflow provides experiment tracking, model evaluation, a production model registry, and deployment capabilities. Teams can record parameters, metrics, artifacts, and experiment results, making it easier to compare different runs and reproduce successful models. The model registry provides a central place to manage model versions and production assets.

MLflow also provides extensive capabilities for LLM and agent development. Its observability features capture traces from LLM calls, tool invocations, retrieval operations, and agent decisions. OpenTelemetry compatibility allows teams to connect MLflow with existing observability infrastructure while maintaining visibility into AI application behavior.

The platform includes systematic evaluation tools for measuring AI application quality and identifying regressions. Developers can use built-in metrics and LLM judges or create custom evaluation approaches. MLflow’s Prompt Registry also allows teams to version and compare prompts while connecting prompt versions with application and agent versions.

Another major capability is the MLflow AI Gateway, which provides centralized access to LLM providers and supports routing, governance, and cost controls. MLflow can also deploy models across environments including local infrastructure, Docker, Kubernetes, Databricks, Azure ML, and AWS SageMaker.

MLflow is available as free open-source software under the Apache 2.0 license, allowing organizations to self-host it and retain control over their infrastructure. MLflow Cloud is also currently offered as a free managed option, while commercial managed services can provide additional enterprise capabilities.

Frequently Asked Questions

What is MLflow?

MLflow is an open-source AI engineering platform for machine learning models, LLM applications, and AI agents. It provides tools for tracking, evaluation, observability, prompt management, model deployment, and production operations.

Is MLflow free?

Yes, the open-source MLflow project is free to use under the Apache 2.0 license. MLflow also currently offers a free managed cloud option, while commercial enterprise services can have separate pricing.

What is MLflow used for?

MLflow is used to manage machine learning and AI development workflows from experimentation through production. Common uses include experiment tracking, model management, LLM evaluation, agent tracing, prompt versioning, and model serving.

Does MLflow support LLM applications?

Yes, MLflow provides dedicated capabilities for LLM applications and AI agents. These include tracing, evaluation, prompt management, an AI Gateway, and production monitoring capabilities.

Can MLflow deploy machine learning models?

Yes, MLflow provides model deployment and serving capabilities across multiple environments. Models can be served locally or deployed to platforms including Databricks, AWS SageMaker, Azure ML, Docker-based environments, and Kubernetes.

What is MLflow Tracing?

MLflow Tracing captures detailed execution information from LLM applications and agents. It can record LLM calls, tool usage, retrieval operations, and other agent activities to help developers understand and debug AI systems.

Does MLflow support prompt management?

Yes, MLflow includes a Prompt Registry for managing prompt templates and their versions. Teams can compare prompt versions and connect them with application or agent versions to evaluate changes.

When was MLflow launched?

MLflow was launched in June 2018 as an open-source project created by Databricks. Its original focus was managing the machine learning lifecycle, including experimentation, reproducibility, and model deployment.

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