MLOps: Ship Models to Production

Package, deploy, and monitor models on Google Cloud, from notebook to live service.

Turn a trained model into a service people use: containers, deployment on Cloud Run, monitoring, and retraining. Built around one project you deploy for real.

What you will be able to do

• Containerize and deploy a model as an API
• Monitor performance and data drift
• Automate retraining and releases

Before you start

• A trained model you want to deploy
• Basic command line

Course outline

1. Package the model: A Docker image that serves the model as an API. A container that runs anywhere.
2. Deploy on Cloud Run: A live endpoint on Google Cloud. A URL others can call.
3. Monitor what matters: Performance and data drift monitoring. Alerts before users notice.
4. Automate releases: CI/CD and a retraining pipeline. A model that updates safely.

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