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AWS Certified Machine Learning Study Guide: Specialty (MLS-C01)
(MLS-C01.AE1)/ISBN:978-1-64459-387-5
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.
Here's what you will get
The AWS Certified Machine Learning - Specialty certification validates your understanding of foundational ML concepts, foundations of statistics, data analysis, exploration, feature engineering, and common ML algorithms. In addition to this, this certification focuses on your ability 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.
Lessons
18+ Lessons | 259+ Exercises | 168+ Quizzes | 188+ Flashcards | 89+ Glossary of terms
TestPrep
50+ Pre Assessment Questions | 2+ Full Length Tests | 55+ Post Assessment Questions | 110+ Practice Test Questions
Hand on lab
27+ LiveLab | 28+ Video tutorials | 01:08+ Hours
Need guidance and support? Click here to check our Instructor Led Course.
Here's what you will learn
Download Course OutlineLessons 1: Introduction
- The AWS Certified Machine Learning Specialty Exam
- Study Guide Features
- AWS Certified Machine Learning Specialty Exam Objectives
Lessons 2: AWS AI ML Stack
- 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
Lessons 3: Supporting Services from the AWS Stack
- Storage
- Amazon VPC
- AWS Lambda
- AWS Step Functions
- AWS RoboMaker
- Summary
- Exam Essentials
Lessons 4: Business Understanding
- Phases of ML Workloads
- Business Problem Identification
- Summary
- Exam Essentials
Lessons 5: Framing a Machine Learning Problem
- ML Problem Framing
- Recommended Practices
- Summary
- Exam Essentials
Lessons 6: Data Collection
- Basic Data Concepts
- Data Repositories
- Data Migration to AWS
- Summary
- Exam Essentials
Lessons 7: Data Preparation
- Data Preparation Tools
- Summary
- Exam Essentials
Lessons 8: Feature Engineering
- Feature Engineering Concepts
- Feature Engineering Tools on AWS
- Summary
- Exam Essentials
Lessons 9: Model Training
- Common ML Algorithms
- Local Training and Testing
- Remote Training
- Distributed Training
- Monitoring Training Jobs
- Debugging Training Jobs
- Hyperparameter Optimization
- Summary
- Exam Essentials
Lessons 10: Model Evaluation
- Experiment Management
- Metrics and Visualization
- Summary
- Exam Essentials
Lessons 11: Model Deployment and Inference
- Deployment for AI Services
- Deployment for Amazon SageMaker
- Advanced Deployment Topics
- Summary
- Exam Essentials
Lessons 12: Application Integration
- Integration with On-Premises Systems
- Integration with Cloud Systems
- Integration with Front-End Systems
- Summary
- Exam Essentials
Lessons 13: Operational Excellence Pillar for ML
- Operational Excellence on AWS
- Summary
- Exam Essentials
Lessons 14: Security Pillar
- Security and AWS
- Secure SageMaker Environments
- AI Services Security
- Summary
- Exam Essentials
Lessons 15: Reliability Pillar
- Reliability on AWS
- Change Management for ML
- Failure Management for ML
- Summary
- Exam Essentials
Lessons 16: Performance Efficiency Pillar for ML
- Performance Efficiency for ML on AWS
- Summary
- Exam Essentials
Lessons 17: Cost Optimization Pillar for ML
- Common Design Principles
- Cost Optimization for ML Workloads
- Summary
- Exam Essentials
Lessons 18: Recent Updates in the AWS AI/ML Stack
- New Services and Features Related to AI Services
- New Features Related to Amazon SageMaker
- Summary
- Exam Essentials
Hands-on LAB Activities
AWS AI ML Stack
- Detecting Objects in an Image
- Using Amazon Translate
- Using Amazon Transcribe and Polly
- Using Amazon SageMaker
Supporting Services from the AWS Stack
- Creating an AWS Lambda Function
- Using Step Functions
Data Collection
- Creating an Amazon DynamoDB Table
- Creating a Kinesis Firehose Delivery Stream
Data Preparation
- Using Amazon Athena
- Using AWS Glue
Model Training
- Performing PCA in SageMaker
- 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
Model Deployment and Inference
- Deploying an ML Model using AWS SageMaker
Application Integration
- Creating an AWS Backup
- Creating a Model
Operational Excellence Pillar for ML
- Enabling Versioning in the Amazon S3 Bucket
Security Pillar
- Using Amazon EC2
- Configuring a Key
- Using Amazon SageMaker Notebook Instance
- Attaching an AWS IAM Role to an Instance
Reliability Pillar
- Understanding Production Security
- Creating an Auto Scaling Group
Performance Efficiency Pillar for ML
- Creating an Amazon EFS
Recent Updates in the AWS AI/ML Stack
- Creating an Amazon Redshift Cluster
Exam FAQs
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
USD 300
PSI or Pearson VUE
Multiple choice and multiple response
The exam contains 65 questions.
180 minutes
750
(on a scale of 100-1000)
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.
Usually three years