AWS spent more than seven years building the Machine Learning Specialty certification into the credential serious ML practitioners chased. Then, this year, AWS quietly closed it. The last exam sat on March 31, 2026, and there is no direct successor. Instead, AWS split that territory across four separate certifications, each aimed at a different kind of AI work.<\/p>\n
If you had Machine Learning Specialty on your study plan for this year, that plan needs a rewrite. This guide walks through why AWS made the change, what each of the four replacement paths actually tests, what they cost, and which one fits the job you actually do, backed by real exam specs and current labor market data rather than a generic “which cert should I get” checklist.<\/p>\n
When Machine Learning Specialty launched, most organizations sitting for it were still building and tuning models from scratch. Data scientists trained algorithms, tuned hyperparameters, and evaluated models largely by hand. That was the discipline the exam was built to certify.<\/p>\n
That is not how most AI work happens now. Teams increasingly start from a managed foundation model through Amazon Bedrock or SageMaker’s built-in model catalog and spend their engineering time on integration, deployment, and governance rather than training a model from a blank slate. A single specialty-level exam could not cleanly cover both the old discipline and the new one, so AWS split it in two: one path for people who still do hands-on ML engineering, and one for people who build applications on top of foundation models.<\/p>\n
A few things are worth knowing, confirmed directly on AWS’s certification status page<\/a> for the retired exam:<\/p>\n\n
The Four AWS AI Certifications Filling the Gap<\/h2>\n