mlflow

Purpose

MLflow is an open-source platform for tracking experiments, packaging ML models, and deploying them in the machine learning lifecycle. It helps standardize workflows for reproducibility and collaboration.

When to Use

Use MLflow when managing multiple ML experiments, comparing models, or deploying to production. It's ideal for teams in MLOps pipelines, such as hyperparameter tuning in Jupyter notebooks, or scaling model deployment in cloud environments like AWS or Azure.

Key Capabilities

• Experiment Tracking: Record metrics, parameters, and artifacts for each run.
• Model Packaging: Save models in a standard format (e.g., MLmodel) for easy sharing.
• Model Deployment: Serve models as REST APIs or integrate with platforms like Kubernetes.
• UI and API: Provides a web UI for visualization and a Python API for programmatic access.
• Artifact Storage: Supports backends like S3, Azure Blob, or local files for storing outputs.

Usage Patterns

Start by initializing a tracking server or using the local backend. For a typical workflow, import MLflow in your script, log metrics during training, and register models after evaluation. Always set the tracking URI first (e.g., via environment variable). For production, package models and deploy via MLflow's serving tools. Avoid running experiments without tracking to prevent loss of reproducibility.