mlops-pipeline

MLOps Pipeline

Set up end-to-end MLOps workflows using MLflow for experiment tracking, model versioning, and automated training pipelines.

When to Use

• User needs to track experiments (parameters, metrics, artifacts)
• User wants to version and register models
• User needs to compare runs and select the best model
• User wants to automate a training pipeline with promotion logic
• User needs to serve a model via MLflow

Available Operations

1. Set up MLflow tracking server
2. Log experiments (params, metrics, artifacts, models)
3. Register models in the Model Registry
4. Promote models through stages (Staging -> Production)
5. Build an automated training pipeline with comparison logic
6. Serve a model via MLflow REST API

Multi-Step Workflow

Step 1: Install Dependencies

pip install mlflow scikit-learn pandas boto3

Step 2: Start MLflow Tracking Server (Local Development)

mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./mlflow-artifacts

For production, use a remote tracking URI:

export MLFLOW_TRACKING_URI=http://mlflow.your-domain.com

Step 3: Create an Experiment and Log a Run

import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score, precision_score

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("my-classifier")

with mlflow.start_run(run_name="rf-baseline"):
    # Log parameters
    params = {"n_estimators": 200, "max_depth": 20, "cv_folds": 5}
    mlflow.log_params(params)

    # Train model
    model = RandomForestClassifier(**{k: v for k, v in params.items() if k != "cv_folds"}, random_state=42)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)

    # Log metrics
    metrics = {
        "accuracy": accuracy_score(y_test, y_pred),
        "f1": f1_score(y_test, y_pred, average="weighted"),
        "precision": precision_score(y_test, y_pred, average="weighted"),
    }
    mlflow.log_metrics(metrics)
    print(f"Metrics: {metrics}")

    # Log model
    mlflow.sklearn.log_model(model, "model")
    print(f"Run ID: {mlflow.active_run().info.run_id}")