AWS AI and ML

I work at the intersection of AI, cloud, and DevOps, and the AI side of that kept producing notes worth keeping in one place - which AWS service to reach for, the concepts behind the choice, and what changes once you move from reading about Bedrock to building with it. This series is where those notes live. Chapter 1 (Fundamentals) - concept-focused, assumes no prior AI/ML background. Chapters 2-3 (build) - assume Chapter 1 as background, hands-on with specific AWS services. Chapters land as the hands-on work behind them gets done, not on a fixed schedule. The series Chapter 1 - Fundamentals Algorithm types, performance metrics, AWS AI services, inference options, Bedrock vs SageMaker, and prompt engineering techniques - the concepts I kept returning to. ...

This is Chapter 1 of the AWS AI and ML series. These are the concepts and service distinctions I kept returning to while working with AWS AI services. Writing them down in one place made the concepts stick. Different types of ML algorithms Three broad families, each suited to a different class of problem. Supervised learning - trains on labelled input-output pairs. Two sub-types: Classification - predicts a category (spam/not spam, image labels). Algorithms: logistic regression, decision trees, random forests, SVM, neural networks. Regression - predicts a continuous value (house price, demand forecast). Algorithms: linear regression, gradient boosting, neural networks. Unsupervised learning - finds structure in unlabelled data: ...

AWS AI and ML

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AWS AI and ML - Series Overview

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AWS AI and ML - Fundamentals