MLflow

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, model registry, artifact storage, and LLM tracing evaluation.

When to Use

• Machine Learning Experiment Tracking: Logging parameters, metrics, code versions, and artifacts across runs.
• Model Registry & Governance: Managing model lifecycle stages (Staging, Production, Archived) with lineage.
• LLM Evaluation & Prompt Engineering: Evaluating LLMs, RAG applications, and prompts using MLflow Evaluate.
• Unified Model Deployment: Packaging models into self-contained flavors (Python function, PyTorch, ONNX) for one-click deployment.

Quick Start

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error