OtherSelf paced prep, exam is 180 minutes and 65 questions·$300 for the exam, prep varies from free AWS material to paid courses

AWS Certified Machine Learning - Specialty (MLS-C01)

3.6

A well respected but ageing certification that is in the process of being succeeded by the newer Machine Learning Engineer Associate. It still proves you can do real ML work on AWS, but the timing question is now the most important part of the decision, so check the retirement status before you commit.

What We Liked

Recognised and respected in enterprise hiring, especially in AWS heavy organisations
Broad and genuinely technical, spanning data engineering, EDA, modeling and operations
Forces you to understand the full machine learning lifecycle, not just model training
Strong signal for cloud ML roles where SageMaker fluency is expected

What Could Be Better

It is being superseded by the Machine Learning Engineer Associate, so its long term future is uncertain
The exam is hard and assumes solid prior ML knowledge, this is not a beginner credential
Heavily SageMaker centric, so a lot of it is about AWS specifics rather than portable ML skill
At three hours and $300 it is a serious commitment of time, money and study

Detailed review

The AWS Certified Machine Learning Specialty has been one of the more credible cloud ML certifications for years, and for good reason, because unlike a lot of vendor exams it does not let you coast on shallow definitions. It is broad and demanding, structured around the real machine learning lifecycle, so you are tested on data engineering, exploratory data analysis, modeling and the operational side of deploying and maintaining models, and it does all of this through the lens of the AWS platform with SageMaker sitting firmly at the centre. Passing it genuinely means something in AWS heavy organisations, where being able to say you understand how to build and run ML on their infrastructure is a real hiring signal, and I have always rated it as a serious rather than a decorative credential. The complication in 2026 is timing, because AWS has introduced the Machine Learning Engineer Associate, MLA-C01, which we review separately, and that newer certification is clearly the direction of travel for the ML certification path.

That reshapes the decision, because you are no longer just asking whether MLS-C01 is good, you are asking whether it is the right one to spend three hours and three hundred dollars on when a successor is establishing itself. My honest guidance is to check the current retirement and availability status directly with AWS before you book anything, since that single fact changes the answer. If you are already deep into AWS, comfortable with SageMaker, and close to exam ready, banking the Specialty while it still runs is defensible and the knowledge itself remains useful. If you are starting your preparation from scratch today, I would lean toward pointing that effort at the Associate instead, because there is little sense investing months into a certification that may be phased out just as you earn it.

Either way, be clear that this is a hard exam that assumes you already know your machine learning, and that a good portion of what you study is AWS specific plumbing rather than transferable theory, which is exactly what you want if your future is on AWS and exactly what you do not want if it is not.

[ final ]

The verdict.

If your career is already on AWS and you want a respected badge, it still holds value, but given the transition toward the newer Machine Learning Engineer Associate I would seriously weigh whether to target that path instead. For a fresh certification in 2026 the Associate is usually the smarter bet, and MLS-C01 makes most sense if you are close to ready and want to bank it before it winds down.