MLflow is an open-source platform designed to manage the complete machine learning (ML) lifecycle, including experimentation, reproducibility, deployment, and a central model registry. It is aimed at data scientists, ML engineers, and MLOps teams working in organisations of all sizes. MLflow is framework-agnostic and integrates with popular ML libraries such as TensorFlow, PyTorch, scikit-learn, and XGBoost. It provides a unified interface to track experiments, package models, and manage their deployment across various environments.

What are the main features of MLflow?

Experiment Tracking

MLflow Tracking enables users to log and query experiments, including code versions, parameters, metrics, and output files. This facilitates comparison of different runs and supports reproducibility.

• Logging: Record parameters, metrics, and artifacts for each run.

• Visualization: Compare results across runs using the MLflow UI.

• APIs: Access tracking functionalities via Python, R, Java, and REST APIs.

MLflow Projects

MLflow Projects provide a standard format for packaging data science code to facilitate reproducibility and reusability.

• Structure: Define projects with a MLproject file specifying dependencies and entry points.

• Environment Management: Use Conda or Docker environments to ensure consistency.

• Version Control: Integrate with Git to track code versions used in experiments.

MLflow Models

MLflow Models offer a convention for packaging and deploying ML models across diverse platforms.

• Flavors: Support for multiple ML libraries, enabling interoperability.

• Deployment: Serve models locally or on cloud platforms using REST APIs.

• Integration: Compatible with tools like Azure ML, Amazon SageMaker, and Kubernetes.

Model Registry

The Model Registry is a centralized store for managing the lifecycle of ML models.

• Versioning: Track multiple versions of a model.

• Stage Transitions: Assign stages such as "Staging" or "Production" to models.

• Annotations: Add descriptions and comments to model versions for better collaboration.

Why choose MLflow?

• Open Source: No vendor lock-in; integrate with existing ML tools and workflows.

• Framework Agnostic: Compatible with various ML libraries and languages.

• Scalable: Suitable for individual users and large teams; supports distributed training.

• Community Support: Backed by a vibrant community and extensive documentation.

• Extensible: Plugin architecture allows customization to fit specific needs.