Shreyas Subramanian, Stefan Natu

AWS Certified Machine Learning Study Guide (eBook, ePUB)

Specialty (MLS-C01) Exam

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: For Dummies
• Seitenzahl: 352
• Erscheinungstermin: 19. November 2021
• Englisch
• ISBN-13: 9781119821014
• Artikelnr.: 63044021

Autorenporträt

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.

Inhaltsangabe

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 17