AWS Certified Machine Learning Study Guide : specialty (mls-c01) exam / Shreyas Subramanian.

Format:

Book

Author/Creator:

Subramanian, Shreyas, author.

Language:

English

Subjects (All):

Machine learning--Examinations--Study guides.

Machine learning.

Cloud computing--Examinations--Study guides.

Cloud computing.

Artificial intelligence--Examinations--Study guides.

Artificial intelligence.

Amazon Web Services (Firm)--Examinations--Study guides.

Amazon Web Services (Firm).

Physical Description:

1 online resource (355 pages)

Edition:

1st edition.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Incorporated, [2022]

Summary:

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.

Contents:

Cover

Title Page

Copyright Page

Acknowledgments

About the Authors

About the Technical Editor

Contents at a Glance

Contents

Introduction

The AWS Certified Machine Learning Specialty Exam

Who Should Buy This Book

Study Guide Features

AWS Certified Machine Learning Specialty Exam Objectives

Assessment Test

Answers to Assessment Test

Part I Introduction

Chapter 1 AWS AI ML Stack

Amazon Rekognition

Image and Video Operations

Amazon Textract

Sync and Async APIs

Amazon Transcribe

Transcribe Features

Transcribe Medical

Amazon Translate

Amazon Translate Features

Amazon Polly

Amazon Lex

Lex Concepts

Amazon Kendra

How Kendra Works

Amazon Personalize

Amazon Forecast

Forecasting Metrics

Amazon Comprehend

Amazon CodeGuru

Amazon Augmented AI

Amazon SageMaker

Analyzing and Preprocessing Data

Training

Model Inference

AWS Machine Learning Devices

Summary

Exam Essentials

Review Questions

Chapter 2 Supporting Services from the AWS Stack

Storage

Amazon S3

Amazon EFS

Amazon FSx for Lustre

Data Versioning

Amazon VPC

AWS Lambda

AWS Step Functions

AWS RoboMaker

Part II Phases of Machine Learning Workloads

Chapter 3 Business Understanding

Phases of ML Workloads

Business Problem Identification

Chapter 4 Framing a Machine Learning Problem

ML Problem Framing

Recommended Practices

Chapter 5 Data Collection

Basic Data Concepts

Data Repositories

Data Migration to AWS

Batch Data Collection

Streaming Data Collection

Chapter 6 Data Preparation

Data Preparation Tools.

SageMaker Ground Truth

Amazon EMR

Amazon SageMaker Processing

AWS Glue

Amazon Athena

Redshift Spectrum

Chapter 7 Feature Engineering

Feature Engineering Concepts

Feature Engineering for Tabular Data

Feature Engineering for Unstructured and Time Series Data

Feature Engineering Tools on AWS

Chapter 8 Model Training

Common ML Algorithms

Supervised Machine Learning

Textual Data

Image Analysis

Unsupervised Machine Learning

Reinforcement Learning

Local Training and Testing

Remote Training

Distributed Training

Monitoring Training Jobs

Amazon CloudWatch

AWS CloudTrail

Amazon EventBridge

Debugging Training Jobs

Hyperparameter Optimization

Chapter 9 Model Evaluation

Experiment Management

Metrics and Visualization

Metrics in AWS AI/ML Services

Chapter 10 Model Deployment and Inference

Deployment for AI Services

Deployment for Amazon SageMaker

SageMaker Hosting: Under the Hood

Advanced Deployment Topics

Autoscaling Endpoints

Deployment Strategies

Testing Strategies

Chapter 11 Application Integration

Integration with On-Premises Systems

Integration with Cloud Systems

Integration with Front-End Systems

Part III Machine Learning Well-Architected Lens

Chapter 12 Operational Excellence Pillar for ML

Operational Excellence on AWS

Everything as Code

Continuous Integration and Continuous Delivery

Continuous Monitoring

Continuous Improvement

Chapter 13 Security Pillar.

Security and AWS

Data Protection

Isolation of Compute

Fine-Grained Access Controls

Audit and Logging

Compliance Scope

Secure SageMaker Environments

Authentication and Authorization

Network Isolation

Logging and Monitoring

AI Services Security

Chapter 14 Reliability Pillar

Reliability on AWS

Change Management for ML

Failure Management for ML

Chapter 15 Performance Efficiency Pillar for ML

Performance Efficiency for ML on AWS

Selection

Review

Monitoring

Trade-offs

Chapter 16 Cost Optimization Pillar for ML

Common Design Principles

Cost Optimization for ML Workloads

Design Principles

Common Cost Optimization Strategies

Chapter 17 Recent Updates in the AWS AI/ML Stack

New Services and Features Related to AI Services

New Services

New Features of Existing Services

New Features Related to Amazon SageMaker

Amazon SageMaker Studio

Amazon SageMaker Data Wrangler

Amazon SageMaker Feature Store

Amazon SageMaker Clarify

Amazon SageMaker Autopilot

Amazon SageMaker JumpStart

Amazon SageMaker Debugger

Amazon SageMaker Distributed Training Libraries

Amazon SageMaker Pipelines and Projects

Amazon SageMaker Model Monitor

Amazon SageMaker Edge Manager

Amazon SageMaker Asynchronous Inference

Appendix Answers to the Review Questions

Chapter 1: AWS AI ML Stack

Chapter 2: Supporting Services from the AWS Stack

Chapter 3: Business Understanding

Chapter 4: Framing a Machine Learning Problem

Chapter 5: Data Collection

Chapter 6: Data Preparation.

Chapter 7: Feature Engineering

Chapter 8: Model Training

Chapter 9: Model Evaluation

Chapter 10: Model Deployment and Inference

Chapter 11: Application Integration

Chapter 12: Operational Excellence Pillar for ML

Chapter 13: Security Pillar

Chapter 14: Reliability Pillar

Chapter 15: Performance Efficiency Pillar for ML

Chapter 16: Cost Optimization Pillar for ML

Index

EULA.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781119821021

1119821029

9781119821014

1119821010

OCLC:

1289816199