MLOps with MLflow

pip install mlflow
mlflow ui  # Start tracking server at localhost:5000

import mlflow

mlflow.set_experiment("my-classification")

with mlflow.start_run():
    # Log parameters
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_param("epochs", 100)
    
    # Train model...
    
    # Log metrics
    mlflow.log_metric("accuracy", 0.95)
    mlflow.log_metric("f1_score", 0.93)
    
    # Log model
    mlflow.sklearn.log_model(model, "model")
    
    # Log artifacts
    mlflow.log_artifact("confusion_matrix.png")

import mlflow.sklearn

mlflow.sklearn.autolog()

# All sklearn metrics logged automatically
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Register a model
mlflow.register_model("runs:/abc123/model", "ProductionClassifier")

# Load from registry
model = mlflow.pyfunc.load_model("models:/ProductionClassifier/Production")

# Serve model as REST API
mlflow models serve -m "models:/ProductionClassifier/1" -p 5001

# Query the API
curl -X POST http://localhost:5001/invocations \
  -H "Content-Type: application/json" \
  -d '{"inputs": [1.0, 2.0, 3.0, 4.0]}'

my_project/
├── MLproject           # Project definition
├── conda.yaml          # Environment
├── train.py            # Training script
└── data/

# MLproject
name: my_project
conda_env: conda.yaml
entry_points:
  main:
    parameters:
      learning_rate: {type: float, default: 0.01}
    command: "python train.py --lr {learning_rate}"