MLOps Capstone Project in AI & Artificial Intelligence

Your mission is to build a 'Self-Healing' ML pipeline. When a data scientist pushes new code or data, your system must: 1) Run unit and data validation tests, 2) Train and evaluate the model, 3) Build a production Docker image, and 4) Deploy it to a staging environment. If the model passes a shadow-deployment period with no drift detected, it is automatically promoted to production.

A model is only as good as its last prediction. As part of your capstone, you will implement a Prometheus and Grafana stack that monitors the 'Golden Signals.' You will create a simulation of Data Drift (e.g., injecting corrupted or shifted input data) and demonstrate that your system's alerting rules catch the problem and notify the team before the model's performance degrades significantly.

To pass the capstone, your system must demonstrate Full Reproducibility. You must be able to 'time travel' to any previous version of your model and prove that you can reconstruct the exact environment, code, and data used to build it. This level of accountability is what separates a hobbyist from a professional MLOps Engineer ready for the most demanding industries.

Welcome to the final challenge. In this Capstone, you will build a complete, end-to-end MLOps pipeline: from versioned data to an automated, monitored production server.

Your goal is to build a system where a single git push triggers data validation, model training, Dockerization, and deployment with live monitoring.

You'll start by defining your Dockerfile and Compose stack. Then, you'll write the GitHub Action that automates the whole journey.

Capstone Check: In a full MLOps pipeline, what should happen if a new model has lower accuracy than the current production model?

• →Deploy it and fix it later
• →The CI pipeline should automatically fail and halt the deployment

Finally, you'll set up a Grafana dashboard to track the model's performance in real-time. You'll even simulate 'Data Drift' to see if your system catches it.

Once complete, you will have built a world-class AI infrastructure. You are now an MLOps Engineer. Congratulations!

Final Check: The most important goal of MLOps is...

• →To write the most complex code possible
• →To make ML deployments reproducible, reliable, and automated

MLOps Capstone complete! You've mastered the machinery that powers the AI revolution. Go forth and deploy!

Gate a Real Pipeline on Health Metrics. Finish the health check that flags a pipeline as degraded when latency or error rate cross their limits.