It's time to build the factory. In this final project, you will integrate every tool and technique from the MLOps track into a single automated system.
1The Automated Pipeline
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.
# MLOps Capstone: End-to-End Deployment
# The Ultimate Operations Integration2The Observability Stack
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.
Pipeline_Flow = {
"Data": "DVC Versioned",
"CI": "GitHub Actions (Test/Build)",
"Serving": "FastAPI + Docker",
"Monitoring": "Prometheus/Grafana"
}3Enterprise Reliability
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.
name: Final-Capstone-Deploy
on: [push]
jobs:
deploy-to-prod:
runs-on: ubuntu-latest
steps: [...]