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
Skills You’ll Get
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
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