From Jupyter to Production: MLOps Pipeline with MLflow and SageMaker

The Data Science Hand-off Problem

Data scientists build brilliant models in Jupyter Notebooks, but handing a notebook to a DevOps engineer to deploy to production is a fragile, broken process.

MLOps applies software engineering rigor to machine learning.

Tracking and Versioning with MLflow

We utilize MLflow to track every experiment, logging hyperparameters and metrics. This ensures reproducibility.

import mlflow

with mlflow.start_run():
    model = train_model(params)
    mlflow.log_params(params)
    mlflow.log_metric("accuracy", accuracy)
    
    # Log the model artifacts directly to the registry
    mlflow.sklearn.log_model(model, "enterprise_fraud_model")

CI/CD for Models

When a model is marked 'Production' in the registry, our GitHub Actions pipeline automatically triggers an AWS SageMaker deployment:

1. Pulls the model artifacts.
2. Builds a standardized inference container.
3. Deploys the container to a SageMaker Endpoint behind an API Gateway.
4. Sets up CloudWatch alarms for model drift detection.

This turns model deployment from a multi-week manual chore into a 10-minute automated pipeline.