MLS-C01.AE1
AWS Certified Machine Learning Specialty Training (MLS-C01)
Learn, prepare and practice for the AWS exam. Gain real-world experience with hands-on Labs and case studies.
- Practice in 26 Hands-On Labs — nothing to install
- 18 Interactive Lessons and 93 topics mapped to the official exam objectives
- 403 Practice Test Questions and 2 Full Length Tests
Intermediate Self-paced · 1 year access 4.7/5 (48 Reviews)
26 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
18Interactive Lessons
93Topics
26LiveLab
403Practice Test Questions
188Flashcards
89Glossary of terms
01 / Skills you'll get
What you will be able to do
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No credit card required
AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) is a preparatory course that provides a structured and detailed learning approach to pass the certification exam. The course focuses on foundational ML concepts, foundations of statistics, data analysis, exploration, feature engineering, and common ML algorithms. In addition to this, it equips you with the skills to deploy those solutions on AWS and to be able to architect an end-to-end solution on AWS from data ingestion to model deployment and monitoring using a host of relevant AWS services for a given business use case. This AWS Machine Learning course comes with an exam-focused approach curriculum that is totally aligned with the latest exam objectives to help you prepare and pass the exam easily.
- Expertise in using AWS AI/ML services like Amazon SageMaker, Amazon Rekognition, and more
- Understanding of ML algorithms like linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and deep learning models
- Data Science pipelines, the entire ML lifecycle
- Awareness of AWS infrastructure services like Amazon S3, Amazon EC2, Amazon RDS, Amazon VPC, and AWS Lambda
- Design and implement scalable and cost-effective cloud architectures for ML applications
- Skilled with deep learning frameworks like TensorFlow and PyTorch, and their application to tasks like image recognition, natural language processing, and time series analysis
- Understanding of reinforcement learning concepts and algorithms
- Tuning hyperparameter to optimize model performance
- Knowledge of ML deployment models as web services, containerized applications, or serverless functions
- Utilizing MLOps for managing the entire ML lifecycle, including version control, continuous integration/continuous delivery (CI/CD), and monitoring
Target Career Roles
- Developers and data scientists
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
18 Interactive Lessons · 93 topics01 Introduction 3 topics +
- The AWS Certified Machine Learning Specialty Exam
- Study Guide Features
- AWS Certified Machine Learning Specialty Exam Objectives
02 AWS AI ML Stack 16 topics · 4 LiveLab +
- Amazon Rekognition
- Amazon Textract
- Amazon Transcribe
- Amazon Translate
- Amazon Polly
- Amazon Lex
- Amazon Kendra
- Amazon Personalize
- Amazon Forecast
- Amazon Comprehend
- Amazon CodeGuru
- Amazon Augmented AI
- Amazon SageMaker
- AWS Machine Learning Devices
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
03 Supporting Services from the AWS Stack 7 topics · 2 LiveLab +
- Storage
- Amazon VPC
- AWS Lambda
- AWS Step Functions
- AWS RoboMaker
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
04 Business Understanding 4 topics +
- Phases of ML Workloads
- Business Problem Identification
- Summary
- Exam Essentials
05 Framing a Machine Learning Problem 4 topics +
- ML Problem Framing
- Recommended Practices
- Summary
- Exam Essentials
06 Data Collection 5 topics · 2 LiveLab +
- Basic Data Concepts
- Data Repositories
- Data Migration to AWS
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
07 Data Preparation 3 topics · 2 LiveLab +
- Data Preparation Tools
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
08 Feature Engineering 4 topics +
- Feature Engineering Concepts
- Feature Engineering Tools on AWS
- Summary
- Exam Essentials
09 Model Training 9 topics · 4 LiveLab +
- Common ML Algorithms
- Local Training and Testing
- Remote Training
- Distributed Training
- Monitoring Training Jobs
- Debugging Training Jobs
- Hyperparameter Optimization
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
10 Model Evaluation 4 topics +
- Experiment Management
- Metrics and Visualization
- Summary
- Exam Essentials
11 Model Deployment and Inference 5 topics · 1 LiveLab +
- Deployment for AI Services
- Deployment for Amazon SageMaker
- Advanced Deployment Topics
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
12 Application Integration 5 topics · 2 LiveLab +
- Integration with On-Premises Systems
- Integration with Cloud Systems
- Integration with Front-End Systems
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
13 Operational Excellence Pillar for ML 3 topics · 1 LiveLab +
- Operational Excellence on AWS
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
14 Security Pillar 5 topics · 4 LiveLab +
- Security and AWS
- Secure SageMaker Environments
- AI Services Security
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
15 Reliability Pillar 5 topics · 2 LiveLab +
- Reliability on AWS
- Change Management for ML
- Failure Management for ML
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
16 Performance Efficiency Pillar for ML 3 topics · 1 LiveLab +
- Performance Efficiency for ML on AWS
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
17 Cost Optimization Pillar for ML 4 topics +
- Common Design Principles
- Cost Optimization for ML Workloads
- Summary
- Exam Essentials
18 Recent Updates in the AWS AI/ML Stack 4 topics · 1 LiveLab +
- New Services and Features Related to AI Services
- New Features Related to Amazon SageMaker
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
26 LiveLabs- Detecting Objects in an Image
- Using Amazon Translate
- Using Amazon Transcribe and Polly
- Using Amazon SageMaker
- Creating an AWS Lambda Function
- Using Step Functions
- Creating an Amazon DynamoDB Table
- Creating a Kinesis Firehose Delivery Stream
- Using Amazon Athena
- Using AWS Glue
- Performing the K-Means Clustering
- Creating Amazon EventBridge Rules that React to Events
- Creating a CloudWatch Dashboard and Adding a Metric to it
- Creating CloudTrail
- Deploying an ML Model Using AWS SageMaker
- Creating an AWS Backup
- Creating a Model
- Enabling Versioning in the Amazon S3 Bucket
- Using Amazon EC2
- Configuring a Key
- Using Amazon SageMaker Notebook Instance
- Attaching an AWS IAM Role to an Instance
- Understanding Production Security
- Creating an Auto Scaling Group
- Creating an Amazon EFS
- Creating an Amazon Redshift Cluster
Labs run in your browser — nothing to install.
03 / Exam details
AWS Certified Machine Learning Specialty Training (MLS-C01) Details
Gain the skills required to pass the AWS ML specialty exam with the AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) course and lab. The lab provides a hands-on learning experience of machine learning in a safe, online environment. The purpose of this course is for you to understand the concepts and principles behind ML, with the practical goal of passing the AWS Certified Machine Learning Specialty exam. This course is intended for professionals who perform a data science, machine learning engineer role.
Questions
65
(per exam)
Duration
180 minutes
(per exam)
Passing Score
750
(on a scale of 1000)
Exam Fee
USD 300
(per exam)
Delivered by
PSI or Pearson VUE
(in-person or online)
Question Format
Multiple choice and multiple response
single/multiple choice, performance-based
Practice Tests
403 Practice Questions
(mapped to official exam objectives)
Certification
MLS-C01.AE1
Amazon credential
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04 / FAQs
Questions before you start
What is the AWS Machine Learning certification? +
MLS-C01 is a certification exam conducted by AWS. It validates your skills for designing, building, training, and deploying machine learning (ML) models on the AWS platform. It is suitable for all those with some ML experience wanting to demonstrate their expertise in using AWS services.
What are the prerequisites for this course? +
There are no formal prerequisites for the AWS Certified Machine Learning Specialty certification. However it’s an advanced-level course that requires a strong foundation in ML Learning concepts. To boost your understanding and speed up the learning process, you can do some base-level courses like AWS Certified Cloud Practitioner & AWS Certified Developer - Associate.
Does this course cover advanced ML concepts? +
Yes, it covers the latest updates in ML learning concepts and provides a comprehensive coverage on several advanced topics. Some of the advanced ML topics include Deep Learning, Reinforcement Learning, Generative Models, Transfer Learning, Time Series Analysis, and Computer Vision.
What is the format of the AWS MLS-C01 exam? +
The exam has 50 questions in total. It has multiple choice and multiple response questions.
How much does the AWS Certified Machine Learning – Specialty exam cost? +
The certification exam price is $300.
What are the professional benefits of earning the AWS certification? +
This AWS certification offers many benefits like career advancement, technical proficiency, credibility, and a competitive advantage, making it a valuable asset for you. If you want to advance your careers in machine learning and cloud computing this can be the ideal starting point for you.
What is the salary range of an AWS certified ML professional? +
The expected annual salary range of an AWS certified professional is anywhere between 136,500 USD to 198,500 USD.
What are the prerequisites for this exam?+
Before you take this exam, it is recommended to have:
- At least two years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud
- Ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- Ability to follow model-training, deployment, and operational best practices
What is the exam registration fee?+
USD 300
Where do I take the exam?+
PSI or Pearson VUE
What is the format of the exam?+
Multiple choice and multiple response
How many questions are asked in the exam?+
The exam contains 65 questions.
What is the duration of the exam?+
180 minutes
What is the passing score?+
750
(on a scale of 100-1000)
What is the exam's retake policy?+
In the event that you do not pass to pass an AWS Certification exam, you may retake the exam subject to the following conditions:
- You must wait 14 days from the day you fail to take the exam again.
- Candidates must pay the exam price each time they attempt the exam.
What is the validity of the certification?+
Usually three years
Where can I find more information about this exam?+
Keep going
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Upscale your professional journey to the next level with this MLS-C01 prep course.
- 1 year of full access
- 26 LiveLab included
- Certificate of completion
No credit card required