MLOps on AWS: SageMaker Training Pipeline, Model Registry & GitHub Actions Explained End-to-End

In this video, you’ll learn how to build an end-to-end MLOps workflow on AWS using Amazon SageMaker. We start with training data stored in Amazon S3, trigger automated machine learning pipelines through GitHub Actions, train models using SageMaker Training Pipelines, store and version models in SageMaker Model Registry, and review model performance inside SageMaker Studio.

This tutorial is designed for AWS Cloud Engineers, DevOps Engineers, MLOps Engineers, Data Scientists, and anyone looking to automate the machine learning lifecycle in production.

What You’ll Learn

✔ What is a SageMaker Training Pipeline
✔ Why MLOps automation is important
✔ How GitHub Actions trigger ML workflows
✔ Using Amazon S3 for training input and model output
✔ Registering models in SageMaker Model Registry
✔ Model versioning and governance
✔ Reviewing model metrics in SageMaker Studio
✔ End-to-end AWS MLOps architecture walkthrough

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