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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:
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.
Résumé
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:
Contenu
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 EventBridge 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 <...