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AWS Certified Machine Learning Study Guide. Specialty (MLS-C01) Exam. Edition No. 1

  • Book

  • 352 Pages
  • February 2022
  • John Wiley and Sons Ltd
  • ID: 5841221

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

Authors

Shreyas Subramanian Stefan Natu