Comet
Experiment tracking, model registry & production monitoring for ML teams
MLflow
Unified platform for tracking, evaluating, and deploying AI models
MLflow provides end‑to‑end experiment tracking, observability, prompt management, evaluation, and model registry, enabling data scientists and GenAI developers to build, compare, and deploy AI applications confidently.
Overview
Overview
MLflow is a developer‑focused platform that brings together experiment tracking, model registry, and deployment tools for both traditional machine learning and generative AI workloads. It supports Python, Java, R, and TypeScript/JavaScript, and integrates natively with popular frameworks such as scikit‑learn, LangChain, and OpenAI.
Capabilities
The platform offers unified experiment tracking with automatic logging of parameters, metrics, and artifacts, as well as LLM‑specific tracing and observability. Built‑in evaluation suites let you benchmark question‑answering or other generative models, while prompt versioning ensures consistency across teams. A centralized model registry and deployment utilities simplify moving models to Docker, Kubernetes, Azure ML, AWS SageMaker, or other environments.
Deployment Flexibility
MLflow can run locally, on‑premise, or in the cloud, and is available as a managed service from major providers like Amazon SageMaker, Azure ML, and Databricks. This flexibility lets organizations adopt the platform at any scale while retaining full control over their infrastructure.
Highlights
Unified experiment tracking, model registry, and deployment across ML and GenAI workloads
Built‑in tracing and observability for LLM/agent applications
Automated evaluation suite for LLMs with integrated metrics
Prompt versioning and reuse to ensure consistency across teams
Pros
- Comprehensive feature set covering the full AI lifecycle
- Multi‑language support (Python, Java, R, TypeScript/JavaScript)
- Can be self‑hosted or used as a managed service on major clouds
- Strong community adoption and extensive integrations
Considerations
- Advanced scaling may require additional infrastructure configuration
- Web UI is functional but less polished than some commercial tools
- Feature store functionality is not included out of the box
- Some integrations need manual setup or custom instrumentation
Managed products teams compare with
When teams consider MLflow, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Data scientists who need reproducible experiment tracking and model versioning
- GenAI developers requiring prompt management and LLM observability
- Teams deploying models to Docker, Kubernetes, or cloud ML services
- Organizations seeking a vendor‑agnostic, extensible AI platform
Not ideal when
- Projects that only need a simple feature store without full lifecycle tools
- Teams looking for an all‑in‑one CI/CD pipeline with zero configuration
- Very small scripts where the overhead of a platform outweighs benefits
- Environments limited to a single programming language without cross‑language needs
How teams use it
Experiment tracking for scikit‑learn models
Automatic logging of parameters, metrics, and artifacts enables easy comparison across runs.
LLM prompt versioning
Maintain consistent prompts across multiple agents and teams, improving collaboration and reproducibility.
Model evaluation for question‑answering
Generate standardized metric reports to compare model performance over time.
Production deployment on Kubernetes
Containerize and serve models with MLflow’s deployment tools, integrating with existing CI/CD pipelines.
Tech snapshot
Python62%
TypeScript25%
JavaScript11%
Java1%
R1%
CSS1%
Frequently asked questions
How do I install MLflow?
Run `pip install mlflow` to install the Python package.
Which programming languages are supported?
MLflow provides SDKs for Python, Java, R, and TypeScript/JavaScript.
Can I host MLflow on my own infrastructure?
Yes, it can run locally, on‑premise, or in any cloud environment.
Does MLflow integrate with major cloud providers?
Managed services are offered by Amazon SageMaker, Azure ML, Databricks, and others.
How do I enable LLM tracing?
Call the appropriate `mlflow.<library>.autolog()` (e.g., `mlflow.openai.autolog()`) before running your model.
Project at a glance
Active- Stars
- 23,738
- Watchers
- 23,738
- Forks
- 5,167
LicenseApache-2.0
Repo age7 years old
Last commit2 days ago
Primary languagePython
Last synced 2 days ago