AWS Certified Machine Learning Study Guide – Speciality (MLS–C01) Exam de S Subramanian

AWS Certified Machine Learning Study Guide – Speciality (MLS–C01) Exam

S Subramanian

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.

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Notă biografică

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.

Cuprins

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