mlflow

# MLflow: ML Lifecycle Management Platform ## When to Use This Skill Use MLflow when you need to: - **Track ML experiments** with parameters, metrics, and artifacts - **Manage model registry** with versioning and stage transitions - **Deploy models** to various platforms (local, cloud, serving) - **Reproduce experiments** with project configurations - **Compare model versions** and performance metrics - **Collaborate** on ML projects with team workflows - **Integrate** with any ML framework (framework-agnostic) **Users**: 20,000+ organizations | **GitHub Stars**: 23k+ | **License**: Apache 2.0 ## Installation ```bash # Install MLflow pip install mlflow # Install with extras pip install mlflow[extras] # Includes SQLAlchemy, boto3, etc. # Start MLflow UI mlflow ui # Access at http://localhost:5000 ``` ## Quick Start ### Basic Tracking ```python import mlflow # Start a run with mlflow.start_run(): # Log parameters mlflow.log_param("learning_rate", 0.001) mlflow.log_param("batch_size", 32) # Your training code model = train_model() # Log metrics mlflow.log_metric("train_loss", 0.15) mlflow.log_metric("val_accuracy", 0.92) # Log model mlflow.sklearn.log_model(model, "model") ``` ### Autologging (Automatic Tracking) ```python import mlflow from sklearn.ensemble import RandomForestClassifier # Enable autologging mlflow.autolog() # Train (automatically logged) model = RandomForestClassifier(n_estimators=100, max_depth=5) mod...