Building a Machine Learning-Ready Organization
AWS Skill Builder
Last updated June 11, 2025
Building a Machine Learning-Ready Organization is a free, 30-minute course about the organizational side of adopting machine learning — getting the company ready, rather than building any model. It is aimed at decision-makers and stays conceptual throughout. The course is organized around four questions: how to prepare an organization to use ML, how to evaluate its data strategy, how to build a culture of learning and collaboration, and how to actually start the ML journey. The emphasis is on sustaining success, not just launching once — adapting the organization so that ML adoption holds rather than stalling after a first project. It closes by pointing to a range of AWS services that organizations draw on as they put ML to work.
What you'll learn
- Preparing an organization to adopt ML
- Evaluating and shaping a data strategy for ML
- Building a culture of learning and collaboration
- How to start — and sustain — the ML journey
- Where AWS services support organizations putting ML to work
Frequently asked questions about Building a Machine Learning-Ready Organization
Who is Building a Machine Learning-Ready Organization for?
Nontechnical business leaders and decision-makers planning or leading ML adoption who want a practical framework for getting their organization, data, and people ready.
Is Building a Machine Learning-Ready Organization free?
Yes — Building a Machine Learning-Ready Organization is completely free to take.
What are the prerequisites for Building a Machine Learning-Ready Organization?
Introductory ML and ML project-planning courses recommended first.
Why we suggest this course
For business decision-makers who already understand what machine learning is and now need a structured way to ready their organization for it — covering preparation, data strategy, culture, and how to begin. The focus on sustaining adoption, not just starting it, is the useful angle. Two things worth knowing: it assumes you have done introductory and project-planning ML courses first (it is the last of a three-part decision-maker set); and it is built by AWS, so its later guidance names several of Amazon's own services (SageMaker, Comprehend, Forecast, Fraud Detector, Kendra, Rekognition) — the organizational principles themselves are general.