Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide
As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions.
The AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture.
From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam.
You’ll also find:
- An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud
- Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science
- Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms
AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning.
Table of Contents:
Introduction xvii
Assessment Test xxix
Answers to Assessment Test xxxv
Part I Introduction 1
Chapter 1 AWS AI ML Stack 3
Amazon Rekognition 4
Image and Video Operations 6
Amazon Textract 10
Sync and Async APIs 11
Amazon Transcribe 13
Transcribe Features 13
Transcribe Medical 14
Amazon Translate 15
Amazon Translate Features 16
Amazon Polly 17
Amazon Lex 19
Lex Concepts 19
Amazon Kendra 21
How Kendra Works 22
Amazon Personalize 23
Amazon Forecast 27
Forecasting Metrics 30
Amazon Comprehend 32
Amazon CodeGuru 33
Amazon Augmented AI 34
Amazon SageMaker 35
Analyzing and Preprocessing Data 36
Training 39
Model Inference 40
AWS Machine Learning Devices 42
Summary 43
Exam Essentials 43
Review Questions 44
Chapter 2 Supporting Services from the AWS Stack 49
Storage 50
Amazon S3 50
Amazon EFS 52
Amazon FSx for Lustre 52
Data Versioning 53
Amazon VPC 54
AWS Lambda 56
AWS Step Functions 59
AWS RoboMaker 60
Summary 62
Exam Essentials 62
Review Questions 63
Part II Phases of Machine Learning Workloads 67
Chapter 3 Business Understanding 69
Phases of ML Workloads 70
Business Problem Identification 71
Summary 72
Exam Essentials 73
Review Questions 74
Chapter 4 Framing a Machine Learning Problem 77
ML Problem Framing 78
Recommended Practices 80
Summary 81
Exam Essentials 81
Review Questions 82
Chapter 5 Data Collection 85
Basic Data Concepts 86
Data Repositories 88
Data Migration to AWS 89
Batch Data Collection 89
Streaming Data Collection 92
Summary 96
Exam Essentials 96
Review Questions 98
Chapter 6 Data Preparation 101
Data Preparation Tools 102
SageMaker Ground Truth 102
Amazon EMR 104
Amazon SageMaker Processing 105
AWS Glue 105
Amazon Athena 107
Redshift Spectrum 107
Summary 107
Exam Essentials 107
Review Questions 109
Chapter 7 Feature Engineering 113
Feature Engineering Concepts 114
Feature Engineering for Tabular Data 114
Feature Engineering for Unstructured and Time Series Data 119
Feature Engineering Tools on AWS 120
Summary 121
Exam Essentials 121
Review Questions 123
Chapter 8 Model Training 127
Common ML Algorithms 128
Supervised Machine Learning 129
Textual Data 138
Image Analysis 141
Unsupervised Machine Learning 142
Reinforcement Learning 146
Local Training and Testing 147
Remote Training 149
Distributed Training 150
Monitoring Training Jobs 154
Amazon CloudWatch 155
AWS CloudTrail 155
Amazon Event Bridge 158
Debugging Training Jobs 158
Hyperparameter Optimization 159
Summary 162
Exam Essentials 162
Review Questions 164
Chapter 9 Model Evaluation 167
Experiment Management 168
Metrics and Visualization 169
Metrics in AWS AI/ML Services 173
Summary 174
Exam Essentials 175
Review Questions 176
Chapter 10 Model Deployment and Inference 181
Deployment for AI Services 182
Deployment for Amazon SageMaker 184
SageMaker Hosting: Under the Hood 184
Advanced Deployment Topics 187
Autoscaling Endpoints 187
Deployment Strategies 188
Testing Strategies 190
Summary 191
Exam Essentials 191
Review Questions 192
Chapter 11 Application Integration 195
Integration with On-Premises
Systems 196
Integration with Cloud Systems 198
Integration with Front-End
Systems 200
Summary 200
Exam Essentials 201
Review Questions 202
Part III Machine Learning Well-Architected Lens 205
Chapter 12 Operational Excellence Pillar for ML 207
Operational Excellence on AWS 208
Everything as Code 209
Continuous Integration and Continuous Delivery 210
Continuous Monitoring 213
Continuous Improvement 214
Summary 215
Exam Essentials 215
Review Questions 217
Chapter 13 Security Pillar 221
Security and AWS 222
Data Protection 223
Isolation of Compute 224
Fine-Grained
Access Controls 225
Audit and Logging 226
Compliance Scope 227
Secure SageMaker Environments 228
Authentication and Authorization 228
Data Protection 231
Network Isolation 232
Logging and Monitoring 233
Compliance Scope 235
AI Services Security 235
Summary 236
Exam Essentials 236
Review Questions 238
Chapter 14 Reliability Pillar 241
Reliability on AWS 242
Change Management for ML 242
Failure Management for ML 245
Summary 246
Exam Essentials 246
Review Questions 247
Chapter 15 Performance Efficiency Pillar for ML 251
Performance Efficiency for ML on AWS 252
Selection 253
Review 254
Monitoring 255
Trade-offs
256
Summary 257
Exam Essentials 257
Review Questions 258
Chapter 16 Cost Optimization Pillar for ML 261
Common Design Principles 262
Cost Optimization for ML Workloads 263
Design Principles 263
Common Cost Optimization Strategies 264
Summary 266
Exam Essentials 266
Review Questions 267
Chapter 17 Recent Updates in the AWS AI/ML Stack 271
New Services and Features Related to AI Services 272
New Services 272
New Features of Existing Services 275
New Features Related to Amazon SageMaker 279
Amazon SageMaker Studio 279
Amazon SageMaker Data Wrangler 279
Amazon SageMaker Feature Store 280
Amazon SageMaker Clarify 281
Amazon SageMaker Autopilot 282
Amazon SageMaker JumpStart 283
Amazon SageMaker Debugger 283
Amazon SageMaker Distributed Training Libraries 284
Amazon SageMaker Pipelines and Projects 284
Amazon SageMaker Model Monitor 284
Amazon SageMaker Edge Manager 285
Amazon SageMaker Asynchronous Inference 285
Summary 285
Exam Essentials 285
Appendix Answers to the Review Questions 287
Chapter 1: AWS AI ML Stack 288
Chapter 2: Supporting Services from the AWS Stack 289
Chapter 3: Business Understanding 290
Chapter 4: Framing a Machine Learning Problem 291
Chapter 5: Data Collection 291
Chapter 6: Data Preparation 292
Chapter 7: Feature Engineering 293
Chapter 8: Model Training 294
Chapter 9: Model Evaluation 295
Chapter 10: Model Deployment and Inference 295
Chapter 11: Application Integration 296
Chapter 12: Operational Excellence Pillar for ML 297
Chapter 13: Security Pillar 298
Chapter 14: Reliability Pillar 298
Chapter 15: Performance Efficiency Pillar for ML 299
Chapter 16: Cost Optimization Pillar for ML 300
Index 303
About the Author :
ABOUT THE AUTHORs
Shreyas Subramanian, PhD, is Principal Machine Learning specialist at Amazon Web Services. He has worked with several enterprise companies on business-critical machine learning and optimization problems.
Stefan Natu is Principal Machine Learning Specialist at Alexa AI, prior to which he was a Principal Architect at Amazon Web Services. His professional focus is on financial services, and he helps customers architect ML use cases on AWS with an emphasis on security, enterprise model governance, and operationalizing machine learning models.
