Developing Machine Learning Solutions

AWS Skill Builder

FreeBeginner1 hourSelf-pacedNo coding

Last updated February 27, 2026

Developing Machine Learning Solutions is a free, one-hour course that walks through the machine learning lifecycle end to end — from the data a model learns on, to training, to getting a model into production — and shows which AWS service fits each stage. It covers where machine learning models come from in the first place, how to read the metrics that tell you whether a model is any good, and the main ways a trained model is put to work in production. It closes on the fundamentals of MLOps: the practices that keep model development and deployment repeatable rather than ad hoc. The material is conceptual rather than hands-on, delivered through interactive elements, text, illustrative graphics, and knowledge checks.

What you'll learn

  • The ML lifecycle end to end
  • Which AWS service fits each stage
  • The types of training data
  • Where ML models come from / how to source them
  • Reading model performance metrics
  • Putting a model into production + MLOps fundamentals

Frequently asked questions about Developing Machine Learning Solutions

Who is Developing Machine Learning Solutions for?

Anyone interested in machine learning and AI, independent of job role, who wants to understand how an ML project moves from data to a deployed, maintained model.

Is Developing Machine Learning Solutions free?

Yes — Developing Machine Learning Solutions is completely free to take.

What are the prerequisites for Developing Machine Learning Solutions?

Recommends completing a foundational ML and AI course and an AI use-cases course first.

Why we suggest this course

A clear map of the full machine learning lifecycle — data, training, evaluation, deployment, and the MLOps discipline that ties them together — for anyone who wants to understand how ML projects actually get built and run. One thing to know: it is built by AWS and names a specific AWS service (SageMaker, S3, EMR, Glue) at each lifecycle stage, so expect the platform framing — the lifecycle and MLOps concepts themselves are general.

Start Developing Machine Learning Solutions on the provider's site

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