[RETIRED] AWS Certified Machine Learning Specialty 2026
What you'll learn
- What to expect on the AWS Certified Machine Learning Specialty exam
- Amazon SageMaker's built-in machine learning algorithms (XGBoost, BlazingText, Object Detection, etc.)
- Feature engineering techniques, including imputation, outliers, binning, and normalization
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
- Data engineering with S3, Glue, Kinesis, and DynamoDB
- Exploratory data analysis with scikit_learn, Athena, Apache Spark, and EMR
- Deep learning and hyperparameter tuning of deep neural networks
- Automatic model tuning and operations with SageMaker
- L1 and L2 regularization
- Applying security best practices to machine learning pipelines
Requirements
- Associate-level knowledge of AWS services such as EC2
- Some existing familiarity with machine learning
- An AWS account is needed to perform the hands-on lab exercises
Description
NOTE: This specialty-level certification has been retired by AWS. The last day to take this exam was March 31, 2026. You might be looking for the AWS Certified Machine Learning Engineer ASSOCIATE exam instead.
Nervous about passing the AWS Certified Machine Learning - Specialty exam (MLS-C01)? You should be! There's no doubt it's one of the most difficult and coveted AWS certifications. A deep knowledge of AWS and SageMaker isn't enough to pass this one - you also need deep knowledge of machine learning, and the nuances of feature engineering and model tuning that generally aren't taught in books or classrooms. You just can't prepare enough for this one.
This certification prep course is taught by Frank Kane, who spent nine years working at Amazon itself in the field of machine learning. Frank took and passed this exam on the first try, and knows exactly what it takes for you to pass it yourself. Joining Frank in this course is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy.
In addition to the 15-hour video course, a 30-minute quick assessment practice exam is included that consists of the same topics and style as the real exam. You'll also get four hands-on labs that allow you to practice what you've learned, and gain valuable experience in model tuning, feature engineering, and data engineering.
This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we'll cover include:
How generative AI and large language models (LLM's) work, including the Transformer architecture (GPT) and attention-based neural networks (masked self-attention)
Amazon's newest generative AI services: Bedrock, SageMaker JumpStart for Generative AI, CodeWhisperer, and SageMaker Foundation Models
S3 data lakes
AWS Glue and Glue ETL
Kinesis data streams, firehose, and video streams
DynamoDB
Data Pipelines, AWS Batch, and Step Functions
Using scikit_learn
Data science basics
Athena and Quicksight
Elastic MapReduce (EMR)
Apache Spark and MLLib
Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)
Ground Truth
Deep Learning basics
Tuning neural networks and avoiding overfitting
Amazon SageMaker, including SageMaker Studio, SageMaker Model Monitor, SageMaker Autopilot, and SageMaker Debugger.
Regularization techniques
Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
Building recommender systems with Amazon Personalize
Monitoring industrial equipment with Lookout and Monitron
Security best practices with machine learning on AWS
Machine learning is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.
If there's a more comprehensive prep course for the AWS Certified Machine Learning - Specialty exam, we haven't seen it. Enroll now, and gain confidence as you walk into that testing center.
Instructor
My name is Stéphane Maarek, I am passionate about Cloud Computing, and I will be your instructor in this course. I teach about AWS certifications, focusing on helping my students improve their professional proficiencies in AWS.
I have already taught 2,500,000+ students and gotten 800,000+ reviews throughout my career in designing and delivering these certifications and courses!
With AWS becoming the centerpiece of today's modern IT architectures, I have decided it is time for students to learn how to be an AWS Machine Learning Professional. So, let’s kick start the course! You are in good hands!
Instructor
Hey, I'm Frank Kane, and I'm also instructing this course. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, where my specialty was recommender systems and machine learning. As an instructor, I'm best known for my top-selling courses in "big data", data analytics, machine learning, Apache Spark, system design, technical management and career growth, and Elasticsearch.
I've been teaching on Udemy since 2015, where I've reached over one million students all around the world!
I've worked hard to keep this course up to date with the latest developments in AWS machine learning, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready!
This course also comes with:
Lifetime access to all future updates
A responsive instructor in the Q&A Section
Udemy Certificate of Completion Ready for Download
A 30 Day "No Questions Asked" Money Back Guarantee!
Join us in this course if you want to prepare for the AWS Machine Learning Certification and master the AWS platform!
Who this course is for:
- Individuals performing a development or data science role seeking certification in machine learning and AWS.
Instructors
Sundog Education's mission is to make highly valuable career skills in data engineering, data science, generative AI, AWS, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford.
As a senior manager at Amazon during its early years, Frank Kane led large technical teams, managed multi-year projects, and served as a "bar-raiser" - interviewing thousands and hiring hundreds across the company.
Frank holds 26 issued patents in machine learning and personalization. After 9 years at Amazon and IMDb, he founded Sundog Education and has since taught over 1.1 million people in AI, ML, AWS, system design, and tech leadership. Frank has been teaching full-time for over 10 years - but is always building projects on the side with the latest emerging technologies, to better explain them to you!
Our teaching style is to simplify complex concepts - once you cut through all the notation and jargon, most of AI and machine learning is pretty easy to understand. We make these advanced topics accessible to anyone with a modest technical background, always using plain English and examples to drive it home. We make learning easy, hands-on, and fun.
Stephane is a solutions architect, consultant and software developer that has a particular interest in all things related to Cloud & Big Data. He's also a many-times best seller instructor on Udemy for his courses in AWS and Apache Kafka.
[See FAQ below to see in which order you can take my courses]
Stéphane is recognized as an AWS Hero and is an AWS Certified Solutions Architect Professional & AWS Certified DevOps Professional. He loves to teach people how to use the AWS properly, to get them ready for their AWS certifications, and most importantly for the real world.
He also loves Apache Kafka. He used on the Program Committee organizing the Kafka Summit in New York, London and San Francisco. He also was an active member of the Apache Kafka community, and has authored blogs on Medium and the guest blog for Confluent. He also has co-founded Conduktor, a prominent company in the Kafka ecosystem.
During his spare time, Stéphane composes piano music, that he publishes online under the artist name Makera ?
FAQ: In which order should you learn?...
AWS Cloud: Start with AWS Certified Cloud Practitioner or AWS Certified Solutions Architect Associate, then move on to AWS Certified Developer Associate and then AWS Certified SysOps Administrator. Afterwards you can either do AWS Certified Solutions Architect Professional or AWS Certified DevOps Professional, or a specialty certification of your choosing. You can also learn about AI with the AWS Certified AI Practitioner course!
Apache Kafka: Start with Apache Kafka for Beginners, then you can learn Connect, Streams and Schema Registry if you're a developer, and Setup and Monitoring courses if you're an admin. Both tracks are needed to pass the Confluent Kafka certification.
Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. As an Amazon “bar raiser,” he held veto authority over hiring decisions across the company, interviewed over 1,000 candidates, and hired and managed hundreds. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own company, Sundog Software, which has taught over one million students around the world about machine learning, data engineering, and managing engineers.
Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.
Our mission is to make highly valuable skills in machine learning, big data, AI, and data science accessible at prices anyone in the world can afford. Our current online courses have reached over 500,000 students worldwide. Sundog Education CEO, Frank Kane, spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaching others about big data analysis.