Оглавление
Shreyas Subramanian. AWS Certified Machine Learning Study Guide
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
AWS Certified Machine Learning Study Guide. Specialty (MLS-C01) Exam
Acknowledgments
About the Authors
About the Technical Editor
Introduction
The AWS Certified Machine Learning Specialty Exam
Why Become AWS Machine Learning Specialty Certified?
How to Become AWS Machine Learning Specialty Certified
Who Should Buy This Book
Study Guide Features
Interactive Online Learning Environment and Test Bank
Conventions Used in This Book
Real World Scenario. Real-World Scenario
AWS Certified Machine Learning Specialty Exam Objectives
Domain 1: Data Engineering. Subdomain 1.1: Create Data Repositories for Machine Learning
Subdomain 1.2: Identify and Implement a Data Ingestion Solution
Subdomain 1.3: Identify and Implement a Data Transformation Solution
Domain 2: Exploratory Data Analysis. Subdomain 2.1: Sanitize and Prepare Data for Modeling
Subdomain 2.2: Perform Feature Engineering
Subdomain 2.3: Analyze and Visualize Data for Machine Learning
Domain 3: Modeling. Subdomain 3.1: Frame Business Problems as Machine Learning Problems
Subdomain 3.2: Select the Appropriate Model(s) for a Given Machine Learning Problem
Subdomain 3.3: Train Machine Learning Models
Subdomain 3.4: Perform Hyperparameter Optimization
Subdomain 3.5: Evaluate machine learning models
Domain 4: Machine Learning Implementation and Operations. Subdomain 4.1: Frame Build Machine Learning Solutions for Performance, Availability, Scalability, Resiliency, and Fault Tolerance
Subdomain 4.2: Recommend and Implement the Appropriate Machine Learning Services and Features for a Given Problem
Subdomain 4.3: Apply Basic AWS Security Practices to Machine Learning Solutions
Subdomain 4.4: Deploy and Operationalize Machine Learning Solutions
Assessment Test
Answers to Assessment Test
Chapter 1 AWS AI ML Stack
Amazon Rekognition
Image and Video Operations
Real World Scenario. Facial Recognition in Video
Amazon Textract
Sync and Async APIs
Amazon Transcribe
Transcribe Features
Transcribe Medical
Amazon Translate
Amazon Translate Features
Amazon Polly
Amazon Lex
Lex Concepts
Amazon Kendra
How Kendra Works
Amazon Personalize
Amazon Forecast
Forecasting Metrics
Amazon Comprehend
Real World Scenario. Email Parsing Model
Amazon CodeGuru
Amazon Augmented AI
Real World Scenario. Detecting Loan Application Fraud
Amazon SageMaker
Analyzing and Preprocessing Data
SageMaker Notebook Instance
SageMaker Studio
SageMaker Data Wrangler
SageMaker Processing
SageMaker GroundTruth
Training
Model Inference
Real World Scenario. A/B Testing Deployment
AWS Machine Learning Devices
Summary
Exam Essentials
Review Questions
Chapter 2 Supporting Services from the AWS Stack
Storage
Amazon S3
Amazon EFS
Real World Scenario. Sharing Data within a Team of Data Scientists
Amazon FSx for Lustre
Training on Terabytes of Data
Data Versioning
Amazon VPC
AWS Lambda
Real World Scenario. Serverless Object Detection
AWS Step Functions
AWS RoboMaker
Real World Scenario. Simulating a Real-World Factory Setting
Summary
Exam Essentials
Review Questions
Chapter 3 Business Understanding
Phases of ML Workloads
Business Problem Identification
Real World Scenario. Optimizing Flight Path between Cities
Summary
Exam Essentials
Review Questions
Chapter 4 Framing a Machine Learning Problem
ML Problem Framing
Real World Scenario. Warehouse Inventory Demand Forecasting
Real World Scenario. Customer Segmentation for E-commerce
Recommended Practices
Summary
Exam Essentials
Review Questions
Chapter 5 Data Collection
Basic Data Concepts
Data Repositories
Data Migration to AWS
Batch Data Collection
Streaming Data Collection
Kinesis Data Streams
Kinesis Data Firehose
Kinesis Data Analytics
Kinesis Video Streams
Kafka-Based Applications
Summary
Exam Essentials
Review Questions
Chapter 6 Data Preparation
Data Preparation Tools
SageMaker Ground Truth. Real World Scenario. Labeling Street Data for Autonomous Vehicles
Real World Scenario. Labeling Street Data for Autonomous Vehicles
Amazon EMR
Amazon SageMaker Processing
AWS Glue
Amazon Athena
Redshift Spectrum
Summary
Exam Essentials
Review Questions
Chapter 7 Feature Engineering
Feature Engineering Concepts
Feature Engineering for Tabular Data
Feature Engineering for Unstructured and Time Series Data
Feature Engineering Tools on AWS
Summary
Exam Essentials
Review Questions
Chapter 8 Model Training
Common ML Algorithms
Supervised Machine Learning
Linear and Logistic Regression
Factorization Machine
k-Nearest Neighbors
Support Vector Machines
Tree-Based Models
Textual Data
Document Classification with BlazingText
Custom Algorithms such as BERT
Image Analysis
Unsupervised Machine Learning
Principal Component Analysis (PCA)
K-Means Clustering
Anomaly Detection with Random Cut Forest
Topic Modeling with LDA or Neural Topic Model (NTM)
Reinforcement Learning
Local Training and Testing
Remote Training
Distributed Training
Monitoring Training Jobs
Amazon CloudWatch
AWS CloudTrail
Amazon EventBridge
Amazon SageMaker
Augmented AI
Debugging Training Jobs
Hyperparameter Optimization
Summary
Exam Essentials
Review Questions
Chapter 9 Model Evaluation
Experiment Management
Metrics and Visualization
Metrics in AWS AI/ML Services
Summary
Exam Essentials
Review Questions
Chapter 10 Model Deployment and Inference
Deployment for AI Services
Deployment for Amazon SageMaker
SageMaker Hosting: Under the Hood
Advanced Deployment Topics
Autoscaling Endpoints
Deployment Strategies
Testing Strategies
Summary
Exam Essentials
Review Questions
Chapter 11 Application Integration
Integration with On-Premises Systems
Integration with Cloud Systems
Integration with Front-End Systems
Summary
Exam Essentials
Review Questions
Chapter 12 Operational Excellence Pillar for ML
Operational Excellence on AWS
Everything as Code
Continuous Integration and Continuous Delivery
Continuous Monitoring
Continuous Improvement
Summary
Exam Essentials
Review Questions
Chapter 13 Security Pillar
Security and AWS
Data Protection
Isolation of Compute
Fine-Grained Access Controls
Audit and Logging
Compliance Scope
Secure SageMaker Environments
Authentication and Authorization
Data Protection
Network Isolation
Logging and Monitoring
Compliance Scope
AI Services Security
Summary
Exam Essentials
Review Questions
Chapter 14 Reliability Pillar
Reliability on AWS
Change Management for ML
Real World Scenario. Change Management
Failure Management for ML
Summary
Exam Essentials
Review Questions
Chapter 15 Performance Efficiency Pillar for ML
Performance Efficiency for ML on AWS
Selection
Review
Monitoring
Trade-offs
Summary
Exam Essentials
Review Questions
Chapter 16 Cost Optimization Pillar for ML
Common Design Principles
Cost Optimization for ML Workloads
Design Principles
Common Cost Optimization Strategies
Summary
Exam Essentials
Review Questions
Chapter 17 Recent Updates in the AWS AI/ML Stack
New Services and Features Related to AI Services
New Services
Amazon Monitron
Amazon Lookout for Vision
Amazon Lookout for Metrics
Amazon Lookout for Equipment
AWS Panorama
Real World Scenario. Rekognition Custom Labels, SageMaker Models, and Amazon Lookout for Vision
Amazon DevOps Guru
Amazon HealthLake
New Features of Existing Services
New Features Related to Amazon SageMaker
Amazon SageMaker Studio
Amazon SageMaker Data Wrangler
Amazon SageMaker Feature Store
Amazon SageMaker Clarify
Amazon SageMaker Autopilot
Amazon SageMaker JumpStart
Amazon SageMaker Debugger
Amazon SageMaker Distributed Training Libraries
Amazon SageMaker Pipelines and Projects
Amazon SageMaker Model Monitor
Amazon SageMaker Edge Manager
Amazon SageMaker Asynchronous Inference
Summary
Exam Essentials
Appendix Answers to the Review Questions
Chapter 1: AWS AI ML Stack
Chapter 2: Supporting Services from the AWS Stack
Chapter 3: Business Understanding
Chapter 4: Framing a Machine Learning Problem
Chapter 5: Data Collection
Chapter 6: Data Preparation
Chapter 7: Feature Engineering
Chapter 8: Model Training
Chapter 9: Model Evaluation
Chapter 10: Model Deployment and Inference
Chapter 11: Application Integration
Chapter 12: Operational Excellence Pillar for ML
Chapter 13: Security Pillar
Chapter 14: Reliability Pillar
Chapter 15: Performance Efficiency Pillar for ML
Chapter 16: Cost Optimization Pillar for ML
Index. A
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