Artificial Intelligence on Amazon Web Services
uCertify AI-AWS.AJ1
Artificial Intelligence on Amazon Web Services
ISBN: 978-1-64459-215-1uCertify AI-AWS.AJ1
AWS Certified Machine Learning Study Guide: Specialty (MLS-C01)
(MLS-C01.AE1) / ISBN : 978-1-64459-387-5
This course includes
Lessons
TestPrep
Hands-On Labs
AI Tutor (Add-on)
48
Review
About This Course
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.
Skills You’ll 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.
Get the support you need. Enroll in our Instructor-Led Course.
Interactive Lessons
18+ Interactive Lessons | 259+ Exercises | 168+ Quizzes | 188+ Flashcards | 89+ Glossary of terms
Gamified TestPrep
50+ Pre Assessment Questions | 2+ Full Length Tests | 55+ Post Assessment Questions | 110+ Practice Test Questions
Hands-On Labs
26+ LiveLab | 28+ Video tutorials | 01:08+ Hours
1
Introduction
- The AWS Certified Machine Learning Specialty Exam
- Study Guide Features
- AWS Certified Machine Learning Specialty Exam Objectives
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
3
Supporting Services from the AWS Stack
- Storage
- Amazon VPC
- AWS Lambda
- AWS Step Functions
- AWS RoboMaker
- Summary
- Exam Essentials
4
Business Understanding
- Phases of ML Workloads
- Business Problem Identification
- Summary
- Exam Essentials
5
Framing a Machine Learning Problem
- ML Problem Framing
- Recommended Practices
- Summary
- Exam Essentials
6
Data Collection
- Basic Data Concepts
- Data Repositories
- Data Migration to AWS
- Summary
- Exam Essentials
7
Data Preparation
- Data Preparation Tools
- Summary
- Exam Essentials
8
Feature Engineering
- Feature Engineering Concepts
- Feature Engineering Tools on AWS
- Summary
- Exam Essentials
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
10
Model Evaluation
- Experiment Management
- Metrics and Visualization
- Summary
- Exam Essentials
11
Model Deployment and Inference
- Deployment for AI Services
- Deployment for Amazon SageMaker
- Advanced Deployment Topics
- Summary
- Exam Essentials
12
Application Integration
- Integration with On-Premises Systems
- Integration with Cloud Systems
- Integration with Front-End Systems
- Summary
- Exam Essentials
13
Operational Excellence Pillar for ML
- Operational Excellence on AWS
- Summary
- Exam Essentials
14
Security Pillar
- Security and AWS
- Secure SageMaker Environments
- AI Services Security
- Summary
- Exam Essentials
15
Reliability Pillar
- Reliability on AWS
- Change Management for ML
- Failure Management for ML
- Summary
- Exam Essentials
16
Performance Efficiency Pillar for ML
- Performance Efficiency for ML on AWS
- Summary
- Exam Essentials
17
Cost Optimization Pillar for ML
- Common Design Principles
- Cost Optimization for ML Workloads
- Summary
- Exam Essentials
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
2
AWS AI ML Stack
- Detecting Objects in an Image
- Using Amazon Translate
- Using Amazon Transcribe and Polly
- Using Amazon SageMaker
3
Supporting Services from the AWS Stack
- Creating an AWS Lambda Function
- Using Step Functions
6
Data Collection
- Creating an Amazon DynamoDB Table
- Creating a Kinesis Firehose Delivery Stream
7
Data Preparation
- Using Amazon Athena
- Using AWS Glue
9
Model Training
- 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
11
Model Deployment and Inference
- Deploying an ML Model Using AWS SageMaker
12
Application Integration
- Creating an AWS Backup
- Creating a Model
13
Operational Excellence Pillar for ML
- Enabling Versioning in the Amazon S3 Bucket
14
Security Pillar
- Using Amazon EC2
- Configuring a Key
- Using Amazon SageMaker Notebook Instance
- Attaching an AWS IAM Role to an Instance
15
Reliability Pillar
- Understanding Production Security
- Creating an Auto Scaling Group
16
Performance Efficiency Pillar for ML
- Creating an Amazon EFS
18
Recent Updates in the AWS AI/ML Stack
- Creating an Amazon Redshift Cluster
Any questions?Check out the FAQs
Still have unanswered questions and need to get in touch?
Contact Us NowBefore 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