AWS Certified Machine Learning Specialty 2024
by Stephane Maarek & Frank Kane · Udemy
Our Verdict
Consider alternativesSkip this one if your goal is the certification: it is the best-known prep course for the AWS Certified Machine Learning - Specialty (MLS-C01) exam, but AWS retired that exam on March 31, 2026, so the credential it targets can no longer be earned. Co-taught by AWS instructor Stephane Maarek and ex-Amazon ML engineer Frank Kane, it holds a 4.5/5 Udemy rating from tens of thousands of ratings and well over 100,000 students, and it is genuinely comprehensive (about 15 hours, 167 lectures across the four official exam domains plus a Generative AI module) and actively maintained (last updated 3/2026). For anyone still pursuing an AWS ML credential, the same instructors' AWS Certified Machine Learning Engineer - Associate (MLA-C01) course is the appropriate successor. The content remains useful purely as a survey of SageMaker and the AWS ML stack, but it is no longer a route to a live certification.
The course is high quality and accurately built around the official exam blueprint, but the exam it prepares you for (MLS-C01) was retired by AWS on March 31, 2026 — already past as of this review. Buying it to earn the certification is no longer possible, so most learners should take the same instructors' MLA-C01 (Machine Learning Engineer - Associate) course instead. We mark it 'skip' for the certification goal; it remains a reasonable buy only if you specifically want the AWS-ML-stack knowledge and not the credential.
Best for: Practitioners who already hold (or have solid knowledge equivalent to) an AWS associate-level certification, have some real-world ML familiarity, and want a structured tour of the AWS machine-learning stack — SageMaker built-in algorithms, data engineering (S3, Glue, Kinesis, DynamoDB), feature engineering, model tuning/operations, and the high-level AI services (Comprehend, Rekognition, Polly, etc.). Useful for someone wanting a concise overview of how ML is done on AWS, independent of the now-retired exam.
Skip if: Anyone whose goal is to actually earn an AWS ML certification in 2026 — MLS-C01 can no longer be sat, so they should take the MLA-C01 course instead. Also not for AWS beginners (it assumes associate-level AWS knowledge and prior ML exposure), nor for those seeking a deep, first-principles machine-learning education: the ML theory is exam-oriented and high-level, not a substitute for a foundational ML course.
About This Course
Prepare for the AWS ML Specialty exam covering SageMaker, data engineering, modeling, and ML implementation on AWS.
What You'll Learn
Curriculum
4 lectures (~10 min): course orientation, what to expect on the exam, and course materials.
38 lectures (~2h 16m): S3 data lakes, Glue/Glue ETL, Kinesis, DynamoDB, Data Pipelines, AWS Batch, Step Functions — maps to exam Domain 1 (20%).
20 lectures (~2h 35m): scikit-learn, Athena, QuickSight, EMR, Apache Spark/MLlib, feature engineering, Ground Truth — maps to exam Domain 2 (24%).
14 lectures (~1h 36m): deep learning basics, tuning neural networks, regularization, overfitting, evaluation metrics.
35 lectures (~3h 4m): SageMaker built-in algorithms, Studio, Autopilot, Model Monitor, Debugger, automatic model tuning.
13 lectures (~1h 13m): Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, Personalize, Lookout/Monitron.
4 lectures (~27 min): consolidation and a hands-on modeling lab; Modeling overall maps to exam Domain 3 (36%).
13 lectures (~1h 10m): deployment, monitoring, and security best practices — maps to exam Domain 4 (20%).
A 30-minute quick-assessment practice exam matching the topics and style of the real exam.
16 lectures (~1h 52m): Transformer architecture, masked self-attention, Bedrock, SageMaker JumpStart for Generative AI, and foundation models — added to keep pace with newer AWS GenAI services.
Course conclusion and next steps (one additional closing section).
Prerequisites
- Associate-level knowledge of core AWS services (e.g., EC2, S3, IAM)
- Some existing familiarity with machine learning concepts
- An AWS account to run the hands-on lab exercises (may incur small AWS charges)
Instructor
Stephane Maarek & Frank Kane
Instructor · Udemy
Pros & Cons
Pros
- Frank Kane spent nine years as an Amazon ML engineer/manager and passed MLS-C01 himself, paired with Stephane Maarek, one of Udemy's most-followed AWS instructors (2.5M+ students taught).
- Comprehensive and well-organized: ~15 hours / 167 lectures structured exactly around the four official AWS exam domains, with four hands-on labs and a practice assessment.
- Actively maintained and up to date (last updated 3/2026), including a dedicated Generative AI module covering Transformers, Bedrock, and SageMaker JumpStart.
- Very high satisfaction at scale: 4.5/5 across tens of thousands of Udemy ratings and well over 100,000 students, plus a 'Bestseller' badge and a Udemy Certificate of Completion with a 30-day money-back guarantee.
Cons
- The target exam (MLS-C01) was retired by AWS on March 31, 2026, so the certification can no longer be earned — the course's primary purpose is now obsolete for new candidates.
- Machine-learning theory is exam-oriented and fairly high-level; it assumes prior ML familiarity and is not a substitute for a foundational ML course.
- Aggregated learner feedback (e.g., Reddit) commonly notes the included practice questions are limited in volume and weaker than dedicated practice-exam sets, so most candidates supplement them.
- Requires associate-level AWS knowledge and an active AWS account for labs, which can incur small AWS costs and is not beginner-friendly.
Alternatives To Consider
Frequently Asked Questions
Is AWS Certified Machine Learning Specialty 2024 free?
AWS Certified Machine Learning Specialty 2024 is $12.99. Paid Udemy course. List price is high (typically US$120-200) but it is almost always available on frequent Udemy sales for roughly US$10-20 (the catalog has listed around $12.99, a representative sale price); a 30-day money-back guarantee and a Certificate of Completion are included. There is no free-audit option. Note: paying for MLS-C01 prep has limited value now that the exam is retired.
Who is AWS Certified Machine Learning Specialty 2024 for?
Practitioners who already hold (or have solid knowledge equivalent to) an AWS associate-level certification, have some real-world ML familiarity, and want a structured tour of the AWS machine-learning stack — SageMaker built-in algorithms, data engineering (S3, Glue, Kinesis, DynamoDB), feature engineering, model tuning/operations, and the high-level AI services (Comprehend, Rekognition, Polly, etc.). Useful for someone wanting a concise overview of how ML is done on AWS, independent of the now-retired exam.
What will you learn in AWS Certified Machine Learning Specialty 2024?
Amazon SageMaker built-in algorithms (XGBoost, BlazingText, Object Detection, etc.) and SageMaker tooling (Studio, Autopilot, Model Monitor, Debugger); Data engineering on AWS with S3 data lakes, Glue/Glue ETL, Kinesis (data streams, Firehose, video streams), and DynamoDB; Exploratory data analysis using scikit-learn, Athena, QuickSight, Apache Spark/MLlib, and EMR; Feature engineering: imputation, outliers, binning, transforms, encoding, and normalization.
What are the prerequisites for AWS Certified Machine Learning Specialty 2024?
Associate-level knowledge of core AWS services (e.g., EC2, S3, IAM); Some existing familiarity with machine learning concepts; An AWS account to run the hands-on lab exercises (may incur small AWS charges).
Is AWS Certified Machine Learning Specialty 2024 worth it?
The course is high quality and accurately built around the official exam blueprint, but the exam it prepares you for (MLS-C01) was retired by AWS on March 31, 2026 — already past as of this review. Buying it to earn the certification is no longer possible, so most learners should take the same instructors' MLA-C01 (Machine Learning Engineer - Associate) course instead. We mark it 'skip' for the certification goal; it remains a reasonable buy only if you specifically want the AWS-ML-stack knowledge and not the credential.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Udemy's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official Udemy course page (now titled '[RETIRED]'; rating, students, curriculum, requirements, retirement notice)
- Class Central listing (syllabus, instructor bios, certification-retirement note)
- AWS official MLS-C01 exam guide (four content domains and weightings)
- Whizlabs / Tutorials Dojo - MLS-C01 retirement date (last exam day March 31, 2026)
- Successor exam: AWS Certified Machine Learning Engineer - Associate (MLA-C01)