Machine Learning on AWS: A Practical Guide to SageMaker AI and Bedrock
A service-by-service walkthrough of how to build, train, and deploy machine learning on AWS -- from ready-made AI APIs to Amazon SageMaker AI and generative AI with Amazon Bedrock -- with a minimal end-to-end code example.
Machine learning used to require deep expertise in distributed systems, GPU clusters, and research-grade tooling just to get a model into production. AWS has spent the better part of a decade trying to collapse that barrier, building a stack that stretches from fully-managed AI services you can call with a single API request, all the way down to raw compute for teams training frontier-scale models. This article walks through that stack: where to start, which service fits which problem, and a simple code example to get your hands dirty.
The Three Layers of the AWS ML Stack
AWS organizes its machine learning offerings into three broad layers, and understanding them makes the rest of the ecosystem much easier to navigate.
At the top sit the AI services -- fully managed APIs for common tasks like image recognition, text extraction, translation, and forecasting. You don't train anything; you call an endpoint and get a result back.
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