There’s a ticking clock on this one. The AWS Certified Machine Learning – Specialty (MLS-C01) retires on March 31, 2026, making it a time-bounded credential for experienced practitioners who want a recognized benchmark before AWS fully transitions to its next-generation ML certification lineup. The salary data makes the urgency real: Skillsoft’s 2024 IT Skills and Salary Survey (drawn from 651 global respondents) found average compensation for holders at $171,725.

What Is AWS Machine Learning Specialty Certification?

Issued by Amazon Web Services, MLS-C01 validates advanced proficiency in designing, building, and deploying machine learning workloads on the AWS platform. The certification has been available since at least December 2019 and carries a three-year validity period with no annual maintenance fees.

AWS doesn’t publish specialty-specific holder counts, but its broader certification program counted over 1.05 million unique certified individuals globally as of January 2025. What sets this credential apart from cloud-adjacent ML certs is its tight integration with the AWS service layer (particularly Amazon SageMaker) and its focus on production-ready ML systems rather than theoretical competency.

The retirement announcement reflects a deliberate strategic shift. AWS is replacing MLS-C01 with three newer credentials: the AWS Certified Machine Learning Engineer – Associate, the AWS Certified AI Practitioner, and the AWS Certified Generative AI Developer – Professional. If you’re planning to sit the exam, March 31, 2026 is your hard deadline.

Who Should Get AWS Machine Learning Specialty Certified?

This exam targets practitioners with real production experience, not students or career changers building foundational skills.

Machine Learning Engineers with two-plus years of AWS hands-on work are the ideal candidates. If you’ve architected SageMaker pipelines, tuned hyperparameters in production, and managed model endpoints, you’re in the right zone.

Data Scientists who regularly operate within the AWS ecosystem and want formal validation of their ML lifecycle expertise (from data ingestion through model evaluation) will find the credential relevant and achievable.

MLOps Engineers who handle deployment, monitoring, and retraining pipelines on AWS will benefit from the Implementation and Operations domain, which maps directly to their daily work.

Who shouldn’t pursue this: Anyone new to ML, anyone without substantial AWS hands-on time, or anyone whose organization runs primarily on Azure or GCP. The exam doesn’t teach ML fundamentals (it tests whether you already know them deeply. If you’re earlier in your journey, the AWS Certified AI Practitioner or the Machine Learning Engineer – Associate are better starting points.

AWS Machine Learning Specialty Exam Domains and Weights

The MLS-C01 exam covers four domains across the full ML lifecycle, with weights that aren’t evenly distributed. One domain carries more than a third of the exam on its own (and knowing which one shapes your entire study strategy. The widget below maps every domain, weight, and topic cluster from the official AWS exam guide.

AWS Machine Learning Specialty Exam Cost, Format, and Pass Score

The total investment ranges from $300 (self-study, exam only) to over $3,000 if you add authorized instructor-led training. The exam itself is 65 questions, 180 minutes, linear format, with a passing score of 750 on a 1,000-point scale. Retakes cost the same $300, and there’s no attempt limit beyond a mandatory 14-day wait after a failed sitting. The widget below breaks down every cost tier.

AWS Machine Learning Specialty Salary and Job Outlook 2026

ZipRecruiter’s March 2026 data places the U.S. median for AWS ML roles at $143,600, with San Francisco professionals averaging $171,689. The U.S. Bureau of Labor Statistics projects 36% growth for data scientist and ML engineer roles through 2033 (well above the overall labor market average). Top-paying industries include financial services, aerospace and defense, technology, and healthcare. The widget below maps salary ranges by role and region.

AWS Machine Learning Specialty Requirements: Experience and Eligibility

There are no formal prerequisites. AWS imposes no mandatory prior certifications or degree requirements to register. In practice, this is one of the harder specialty exams for anyone who attempts it underprepared.

AWS recommends at least two years of hands-on experience developing, architecting, or running ML and deep learning workloads in the AWS Cloud. That means real production exposure (not sandbox tutorials). Specifically, you should be comfortable with ML algorithm selection and intuition, hyperparameter optimization, model evaluation techniques (regularization, cross-validation, bias-variance tradeoffs), and AWS-specific tooling like SageMaker, S3, and IAM.

Candidates commonly build toward this exam after earning the AWS Certified Solutions Architect – Associate or the AWS Certified Machine Learning Engineer – Associate, though neither is required. The honest difficulty assessment: this is an advanced exam. The Modeling domain alone covers algorithm families, neural network architecture decisions, and production evaluation methods at a level that catches underprepared candidates. Budget time accordingly.

Certification is valid for three years. Recertification requires passing the current exam version (there are no annual fees, no CPE requirements, and no portfolio submissions).

How to Study for AWS Machine Learning Specialty: Resources and Plan

Most candidates need 80–120+ hours of structured preparation, with the split between self-study and guided coursework depending heavily on prior AWS and ML depth. Free starting points include the official exam guide and a 20-question practice set on AWS Skill Builder. The resource navigator and study plan builder below cover the full landscape.

What Changed in the AWS Machine Learning Specialty 2026 Update

There’s no 2026 update (and there won’t be one. MLS-C01 is retiring March 31, 2026, making the current exam guide (v2.4) the final version.

The domain structure and weights are unchanged: Data Engineering (20%), Exploratory Data Analysis (24%), Modeling (36%), and ML Implementation and Operations (20%). No topic additions or removals have been announced for the retiring version.

What the retirement does signal is a content philosophy shift. The original MLS-C01 emphasized training custom models from scratch and deep algorithmic knowledge. The successor certifications move toward managed services, foundation models, and generative AI tooling (areas like Amazon Bedrock that weren’t part of the official MLS-C01 blueprint, even as third-party course creators began incorporating LLM content in late 2024).

For candidates sitting the exam before retirement: existing study materials tied to the v2.4 exam guide remain fully applicable. Don’t let the retirement announcement push you toward newer-format prep materials that may drift from the actual test content. The blueprint is stable. Focus your energy there.

How AI Is Changing Machine Learning Careers

Automation is restructuring ML work, not eliminating it. AutoML tooling and LLM-based code generation are handling routine data preprocessing and model selection tasks that used to require significant manual effort (which pushes experienced practitioners toward higher-order problems: system design, production reliability, cost architecture, and ethical oversight).

The World Economic Forum’s widely cited estimate places AI-related job displacement at 85 million roles by 2027, offset by 97 million new positions (a net gain concentrated in roles requiring human judgment on top of technical execution). For ML practitioners, this translates concretely: the demand isn’t shrinking, but the skills mix is shifting. MLOps, foundation model integration, and cross-functional AI strategy are becoming more central than pure algorithm implementation.

The BLS’s 36% projected growth for data scientist and ML engineer roles through 2033 reflects this dynamic. The MLS-C01 successor certifications (particularly the Generative AI Developer credential) directly address where AWS sees the field heading. Earning MLS-C01 before retirement gives you a production-ML credential; pairing it with an AI Practitioner or ML Engineer – Associate certification positions you for what comes next.

Is AWS Machine Learning Specialty Worth It in 2026?

Yes (if you can sit the exam before March 31, 2026, and you have the two-plus years of AWS ML experience it demands). The credential delivers a documented salary premium and differentiates you in a competitive market for cloud ML roles. The primary alternative is the Google Cloud Professional Machine Learning Engineer, which matches it in difficulty and salary range. The widget below runs the full comparison.

How to Get AWS Machine Learning Specialty Certified: Step by Step

1. Confirm you have 2+ years of hands-on AWS ML/deep learning experience.
2. Download the official MLS-C01 Exam Guide and complete the free AWS Skill Builder practice set.
3. Select your prep path: self-study (Udemy + Whizlabs), structured coursework (Pluralsight or Coursera), or authorized training.
4. Schedule your exam through Pearson VUE before March 31, 2026 (that’s the hard retirement cutoff).
5. Pass with a 750+ scaled score and download your digital badge from the AWS Certification portal. After passing, you’ll receive a 50% discount voucher toward your next AWS certification exam.

The MLS-C01 window closes March 31, 2026. For experienced AWS ML practitioners who want a recognized credential before the landscape shifts entirely, the case is clear. See the full AWS Certification roadmap for what comes after, and explore the TechJacks certification hub for related cloud and AI career guidance.

Reference Resource List

1. AWS Certified Machine Learning – Specialty Exam Guide (MLS-C01)
2. AWS Skill Builder – Official Practice Question Set (MLS-C01)
3. AWS Certified Machine Learning Engineer – Associate
4. AWS Certified AI Practitioner
5. AWS Certified Solutions Architect – Associate
6. Pearson VUE – AWS Testing
7. Skillsoft IT Skills and Salary Survey – Top Paying AWS Certifications
8. ZipRecruiter – AWS Machine Learning Salary
9. ZipRecruiter – AWS Certified Machine Learning Specialist Salary (New York)
10. CSUN Tseng College – Machine Learning Engineer Salary and Job Outlook (BLS source)
11. Infosec Institute – AWS Certified Machine Learning Salary
12. NetCom Learning – Machine Learning Engineer Salary
13. Google Cloud Professional Machine Learning Engineer Certification
14. Microsoft Azure AI Engineer Associate (AI-102)
15. Udemy – AWS Certified Machine Learning Specialty 2026
16. Pluralsight – AWS Certified Machine Learning Specialty Path
17. Coursera – Exam Prep MLS-C01 Specialization
18. Whizlabs – AWS Certifications
19. Wiley/Sybex – AWS Certified Machine Learning Study Guide
20. Packt – AWS Certified Machine Learning Specialty MLS-C01 Certification Guide

Continue Reading

• Browse All 24 IT Certifications — compare exams, salaries, and career paths side by side
• All AWS Certifications — see every AWS credential in one place
• AWS ML Engineer Associate — the successor cert replacing ML Specialty after March 2026
• AWS AI Practitioner — foundational AI cert for those entering the ML track
• IAPP AIGP — AI governance credential for the regulatory and ethics dimension of ML