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AWS Certified Machine Learning Engineer Study Guide
Associate (Mla-C01) Exam
Taschenbuch von Dario Cabianca
Sprache: Englisch

58,00 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 2-3 Werktage ab Escheinungsdatum. Dieses Produkt erscheint am 09.07.2025

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Beschreibung

Prepare for the AWS Machine Learning Engineer certification exam quickly and efficiently

The AWS® Certified Machine Learning Engineer Study Guide is a comprehensive resource for complete coverage of the challenging MLA-C01 exam. This Sybex Study Guide covers all of the MLA-C01 objectives. Prepare for the exam smarter and faster with Sybex thanks to up-to-date and accurate content, including an assessment test that validates and measures exam readiness, an objective map, real-world examples and scenarios, practical exercises, and challenging chapter review questions. Reinforce and remember what you've learned with the Sybex online learning environment and test bank, accessible across multiple devices. Get prepared for the AWS Certified Machine Learning exam with Sybex.

Coverage of 100% of all exam objectives in this Study Guide means you'll be ready for:

  • Data and Feature Engineering
  • Data Science
  • Model Development and Refinement
  • Model Deployment and Orchestration
  • Machine Learning Operations
  • AWS AI Solutions
  • DevOps Engineering

ABOUT THE AWS MACHINE LEARNING ENGINEER - ASSOCIATE CERTIFICATION

The AWS Machine Learning Engineer - Associate certification demonstrates your ability to implement AI and ML solutions in the Amazon Web Services cloud. It tests and verifies your expertise in designing, building, training, tuning, and deploying machine learning models on AWS.

Interactive learning environment

Take your exam prep to the next level with Sybex's superior interactive online study tools. To access our learning environment, simply visit [...] go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to:

  • Interactive test bank with a practice exam to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you're ready to take the certification exam.
  • 100 electronic flashcards to reinforce learning and last-minute prep before the exam
  • Comprehensive glossary in PDF format gives you instant access to the key terms so you are fully prepared

Prepare for the AWS Machine Learning Engineer certification exam quickly and efficiently

The AWS® Certified Machine Learning Engineer Study Guide is a comprehensive resource for complete coverage of the challenging MLA-C01 exam. This Sybex Study Guide covers all of the MLA-C01 objectives. Prepare for the exam smarter and faster with Sybex thanks to up-to-date and accurate content, including an assessment test that validates and measures exam readiness, an objective map, real-world examples and scenarios, practical exercises, and challenging chapter review questions. Reinforce and remember what you've learned with the Sybex online learning environment and test bank, accessible across multiple devices. Get prepared for the AWS Certified Machine Learning exam with Sybex.

Coverage of 100% of all exam objectives in this Study Guide means you'll be ready for:

  • Data and Feature Engineering
  • Data Science
  • Model Development and Refinement
  • Model Deployment and Orchestration
  • Machine Learning Operations
  • AWS AI Solutions
  • DevOps Engineering

ABOUT THE AWS MACHINE LEARNING ENGINEER - ASSOCIATE CERTIFICATION

The AWS Machine Learning Engineer - Associate certification demonstrates your ability to implement AI and ML solutions in the Amazon Web Services cloud. It tests and verifies your expertise in designing, building, training, tuning, and deploying machine learning models on AWS.

Interactive learning environment

Take your exam prep to the next level with Sybex's superior interactive online study tools. To access our learning environment, simply visit [...] go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to:

  • Interactive test bank with a practice exam to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you're ready to take the certification exam.
  • 100 electronic flashcards to reinforce learning and last-minute prep before the exam
  • Comprehensive glossary in PDF format gives you instant access to the key terms so you are fully prepared
Über den Autor

ABOUT THE AUTHOR

DARIO CABIANCA is the AWS Practice Director at Trace3-a leading IT consultancy and AWS Advanced Consulting Partner-offering AI, data, cloud and cybersecurity solutions. He is the author of Google Cloud Platform (GCP) Professional Cloud Security Engineer Certification Companion and Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion. Dario has collaborated with leading global consulting firms and enterprises for over 20 years, delivering impactful solutions in enterprise architecture, cloud computing, cybersecurity, and artificial intelligence.

Inhaltsverzeichnis

Contents

Chapter 1Introduction to Machine Learning1

Understanding Artificial Intelligence2

Data, Information, Knowledge3

Data3

Information4

Knowledge5

Understanding Machine Learning6

ML Lifecycle6

Define ML Problem6

Collect Data8

Process Data8

Choose Algorithm8

Train Model9

Evaluate Model9

Deploy Model9

Derive Inference11

Monitor Model11

ML Concepts11

Features11

Target Variable12

Optimization Problem12

Objective Function13

ML Algorithms vs. ML Models13

Differences Between ML and AI14

Understanding Deep Learning16

Introduction to Neural Networks16

Structure of a Neural Network16

Neuron16

Input Layer18

Hidden Layers18

Output Layer18

How Neural Networks Work18

Neural Networks Types19

Artificial Neural Networks20

Deep Neural Networks20

Convolutional Neural Networks20

Recurrent Neural Networks20

Differences Between DL and ML21

Case Studies21

Case Study 1: Mobileye's Autonomous Driving Technology21

Case Study 2: Leidos' Healthcare ML Applications21

Summary22

Exam Essentials23

Review Questions24

Chapter 2Data Ingestion and Storage27

Introducing Ingestion and Storage28

Ingesting and Storing Data28

Data Formats and Ingestion Techniques31

Choosing AWS Ingestion Services34

Amazon Data Firehose35

Amazon Kinesis Data Streams35

Amazon Managed Streaming for Apache Kafka (MSK)36

Amazon Managed Service for Apache Flink38

AWS DataSync39

AWS Glue40

Choosing AWS Storage Services41

Amazon Simple Storage Service (S3)42

Amazon Elastic File System (EFS)45

Amazon FSx for Lustre47

Amazon FSx for NetApp ONTAP49

Amazon FSx for Windows File Server50

Amazon FSx for OpenZFS51

Amazon Elastic Block Storage (EBS)51

Amazon Relational Database Service (RDS)52

Amazon DynamoDB52

Troubleshooting53

Summary54

Exam Essentials55

Review Questions57

Chapter 4Model Selection61

Understanding AWS AI Services63

Vision64

Amazon Rekognition64

Amazon Textract65

Speech66

Amazon Polly66

Amazon Transcribe67

Language67

Amazon Translate67

Amazon Comprehend68

Chatbot69

Amazon Lex69

Recommendation70

Amazon Personalize70

Generative AI71

Amazon Bedrock71

Developing Models with Amazon SageMaker Built-in Algorithms81

Supervised ML Algorithms81

General Regression and Classification Algorithms83

Recommendation102

Forecasting104

Unsupervised ML Algorithms105

Clustering105

Dimensionality Reduction113

Topic Modeling119

Anomaly Detection121

Textual Analysis123

BlazingText124

Sequence-to-Sequence126

Image Processing127

Image Classification127

Object Detection128

Semantic Segmentation130

Criteria for Model Selection131

Summary132

Exam Essentials133

Review Questions136

Chapter 5Model Training and Evaluation141

Training143

Local Training144

Remote Training145

Distributed Training146

Monitoring Training Jobs147

Debugging Training Jobs148

Hyperparameter Tuning149

Model Parameter and Hyperparameter151

Exploring the Hyperparameter Space with Amazon SageMaker AI Automatic Model Tuning152

Evaluation Metrics154

Classification Problem Metrics154

Regression Problem Metrics160

Hyperparameter Tuning Techniques164

Manual Search164

Grid Search165

Random Search165

Bayesian Search165

Multi-algorithm Optimization166

Managing Bias and Variance Trade-Off166

Addressing Overfitting and Underfitting168

Underfitting168

Overfitting170

Regularization170

Advanced Techniques173

Model Performance Evaluation173

Performance Evaluation Methods173

K-Fold Cross-Validation174

Random Train-Test Split175

Holdout Set176

Bootstrap176

Evaluating Foundation Models177

Automatic Evaluations177

Human Evaluations177

LLM-as-a-Judge177

Programmatic Evaluations177

Knowledge Base Evaluations177

Deep Dive Model Tuning Example177

Summary185

Exam Essentials187

Review Questions190

Chapter 6Model Deployment and Orchestration193

AWS Model Deployment Services194

Deploying AI Services195

Amazon Rekognition196

Amazon Textract197

Amazon Polly197

Amazon Transcribe198

Amazon Comprehend198

Amazon Lex199

Amazon Personalize199

Amazon Bedrock200

Deploying Your Model201

Infrastructure Selection Considerations202

Managed Model Deployments203

Unmanaged Model Deployments211

Optimizing ML Models for Edge Devices216

Advanced Model Deployment Techniques218

Autoscaling Endpoints218

Deployment and Testing Strategies221

Blue/Green Deployment221

Orchestrating ML Workflows227

Introducing Amazon SageMaker Pipelines228

Code Repository and Version Control228

Introducing Amazon SageMaker Model Registry229

CI/CD230

MLOps Orchestration230

AWS Step Functions231

Amazon Managed Workflows for Apache Airflow232

Choosing an Orchestration Tool232

Automating Model Building and Deployment233

Define the Workflow Steps234

Create and Configure Pipeline Steps234

Define the Pipeline237

Set Up Triggers and Schedules237

Execute the Pipeline238

Key Considerations238

Deep-Dive Model Deployment Example238

Summary247

Exam Essentials248

Review Questions250

Chapter 7Model Monitoring and Cost Optimization253

Monitoring Model Inference255

Drifts in Models256

Techniques to Monitor Data Quality and Model Performance257

Monitoring Workflow259

Design Principles for Monitoring261

Operational Excellence Pillar261

Security Pillar262

Reliability Pillar263

Performance Efficiency Pillar264

Cost Optimization Pillar266

Sustainability Pillar269

Monitoring Infrastructure and Cost270

Monitoring and Observability Services271

Amazon CloudWatch Logs Insights272

Amazon EventBridge273

AWS CloudTrail274

AWS X-Ray274

Amazon GuardDuty275

Amazon Inspector276

AWS Security Hub277

Cost Tracking and Optimization Services278

AWS Cost Explorer278

AWS Cost and Usage Reports279

AWS Trusted Advisor280

AWS Budgets280

Pricing Models281

Summary283

Exam Essentials284

Review Questions286

Chapter 8Model Security289

Security Design Principles290

Implement a Strong Identity Foundation290

Apply Security at all Layers291

Enable Traceability292

Protect Your Data (At-Rest, In-Use, and In-Transit)293

Automate Security Processes294

Prepare for Security Events295

Securing AWS Services295

Securing Identities with IAM296

Identities296

Access Policies302

Securing Infrastructure and Data305

Network Isolation with VPC305

Private Connectivity306

Data Protection306

Monitoring and Auditing307

Ensuring Compliance307

Summary308

Exam Essentials309

Review Questions311

Details
Erscheinungsjahr: 2025
Fachbereich: Unterricht
Genre: Erziehung & Bildung, Importe
Rubrik: Sozialwissenschaften
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781394319954
ISBN-10: 1394319959
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Cabianca, Dario
Hersteller: Wiley
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 234 x 187 x 35 mm
Von/Mit: Dario Cabianca
Erscheinungsdatum: 09.07.2025
Gewicht: 0,738 kg
Artikel-ID: 133346528
Über den Autor

ABOUT THE AUTHOR

DARIO CABIANCA is the AWS Practice Director at Trace3-a leading IT consultancy and AWS Advanced Consulting Partner-offering AI, data, cloud and cybersecurity solutions. He is the author of Google Cloud Platform (GCP) Professional Cloud Security Engineer Certification Companion and Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion. Dario has collaborated with leading global consulting firms and enterprises for over 20 years, delivering impactful solutions in enterprise architecture, cloud computing, cybersecurity, and artificial intelligence.

Inhaltsverzeichnis

Contents

Chapter 1Introduction to Machine Learning1

Understanding Artificial Intelligence2

Data, Information, Knowledge3

Data3

Information4

Knowledge5

Understanding Machine Learning6

ML Lifecycle6

Define ML Problem6

Collect Data8

Process Data8

Choose Algorithm8

Train Model9

Evaluate Model9

Deploy Model9

Derive Inference11

Monitor Model11

ML Concepts11

Features11

Target Variable12

Optimization Problem12

Objective Function13

ML Algorithms vs. ML Models13

Differences Between ML and AI14

Understanding Deep Learning16

Introduction to Neural Networks16

Structure of a Neural Network16

Neuron16

Input Layer18

Hidden Layers18

Output Layer18

How Neural Networks Work18

Neural Networks Types19

Artificial Neural Networks20

Deep Neural Networks20

Convolutional Neural Networks20

Recurrent Neural Networks20

Differences Between DL and ML21

Case Studies21

Case Study 1: Mobileye's Autonomous Driving Technology21

Case Study 2: Leidos' Healthcare ML Applications21

Summary22

Exam Essentials23

Review Questions24

Chapter 2Data Ingestion and Storage27

Introducing Ingestion and Storage28

Ingesting and Storing Data28

Data Formats and Ingestion Techniques31

Choosing AWS Ingestion Services34

Amazon Data Firehose35

Amazon Kinesis Data Streams35

Amazon Managed Streaming for Apache Kafka (MSK)36

Amazon Managed Service for Apache Flink38

AWS DataSync39

AWS Glue40

Choosing AWS Storage Services41

Amazon Simple Storage Service (S3)42

Amazon Elastic File System (EFS)45

Amazon FSx for Lustre47

Amazon FSx for NetApp ONTAP49

Amazon FSx for Windows File Server50

Amazon FSx for OpenZFS51

Amazon Elastic Block Storage (EBS)51

Amazon Relational Database Service (RDS)52

Amazon DynamoDB52

Troubleshooting53

Summary54

Exam Essentials55

Review Questions57

Chapter 4Model Selection61

Understanding AWS AI Services63

Vision64

Amazon Rekognition64

Amazon Textract65

Speech66

Amazon Polly66

Amazon Transcribe67

Language67

Amazon Translate67

Amazon Comprehend68

Chatbot69

Amazon Lex69

Recommendation70

Amazon Personalize70

Generative AI71

Amazon Bedrock71

Developing Models with Amazon SageMaker Built-in Algorithms81

Supervised ML Algorithms81

General Regression and Classification Algorithms83

Recommendation102

Forecasting104

Unsupervised ML Algorithms105

Clustering105

Dimensionality Reduction113

Topic Modeling119

Anomaly Detection121

Textual Analysis123

BlazingText124

Sequence-to-Sequence126

Image Processing127

Image Classification127

Object Detection128

Semantic Segmentation130

Criteria for Model Selection131

Summary132

Exam Essentials133

Review Questions136

Chapter 5Model Training and Evaluation141

Training143

Local Training144

Remote Training145

Distributed Training146

Monitoring Training Jobs147

Debugging Training Jobs148

Hyperparameter Tuning149

Model Parameter and Hyperparameter151

Exploring the Hyperparameter Space with Amazon SageMaker AI Automatic Model Tuning152

Evaluation Metrics154

Classification Problem Metrics154

Regression Problem Metrics160

Hyperparameter Tuning Techniques164

Manual Search164

Grid Search165

Random Search165

Bayesian Search165

Multi-algorithm Optimization166

Managing Bias and Variance Trade-Off166

Addressing Overfitting and Underfitting168

Underfitting168

Overfitting170

Regularization170

Advanced Techniques173

Model Performance Evaluation173

Performance Evaluation Methods173

K-Fold Cross-Validation174

Random Train-Test Split175

Holdout Set176

Bootstrap176

Evaluating Foundation Models177

Automatic Evaluations177

Human Evaluations177

LLM-as-a-Judge177

Programmatic Evaluations177

Knowledge Base Evaluations177

Deep Dive Model Tuning Example177

Summary185

Exam Essentials187

Review Questions190

Chapter 6Model Deployment and Orchestration193

AWS Model Deployment Services194

Deploying AI Services195

Amazon Rekognition196

Amazon Textract197

Amazon Polly197

Amazon Transcribe198

Amazon Comprehend198

Amazon Lex199

Amazon Personalize199

Amazon Bedrock200

Deploying Your Model201

Infrastructure Selection Considerations202

Managed Model Deployments203

Unmanaged Model Deployments211

Optimizing ML Models for Edge Devices216

Advanced Model Deployment Techniques218

Autoscaling Endpoints218

Deployment and Testing Strategies221

Blue/Green Deployment221

Orchestrating ML Workflows227

Introducing Amazon SageMaker Pipelines228

Code Repository and Version Control228

Introducing Amazon SageMaker Model Registry229

CI/CD230

MLOps Orchestration230

AWS Step Functions231

Amazon Managed Workflows for Apache Airflow232

Choosing an Orchestration Tool232

Automating Model Building and Deployment233

Define the Workflow Steps234

Create and Configure Pipeline Steps234

Define the Pipeline237

Set Up Triggers and Schedules237

Execute the Pipeline238

Key Considerations238

Deep-Dive Model Deployment Example238

Summary247

Exam Essentials248

Review Questions250

Chapter 7Model Monitoring and Cost Optimization253

Monitoring Model Inference255

Drifts in Models256

Techniques to Monitor Data Quality and Model Performance257

Monitoring Workflow259

Design Principles for Monitoring261

Operational Excellence Pillar261

Security Pillar262

Reliability Pillar263

Performance Efficiency Pillar264

Cost Optimization Pillar266

Sustainability Pillar269

Monitoring Infrastructure and Cost270

Monitoring and Observability Services271

Amazon CloudWatch Logs Insights272

Amazon EventBridge273

AWS CloudTrail274

AWS X-Ray274

Amazon GuardDuty275

Amazon Inspector276

AWS Security Hub277

Cost Tracking and Optimization Services278

AWS Cost Explorer278

AWS Cost and Usage Reports279

AWS Trusted Advisor280

AWS Budgets280

Pricing Models281

Summary283

Exam Essentials284

Review Questions286

Chapter 8Model Security289

Security Design Principles290

Implement a Strong Identity Foundation290

Apply Security at all Layers291

Enable Traceability292

Protect Your Data (At-Rest, In-Use, and In-Transit)293

Automate Security Processes294

Prepare for Security Events295

Securing AWS Services295

Securing Identities with IAM296

Identities296

Access Policies302

Securing Infrastructure and Data305

Network Isolation with VPC305

Private Connectivity306

Data Protection306

Monitoring and Auditing307

Ensuring Compliance307

Summary308

Exam Essentials309

Review Questions311

Details
Erscheinungsjahr: 2025
Fachbereich: Unterricht
Genre: Erziehung & Bildung, Importe
Rubrik: Sozialwissenschaften
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781394319954
ISBN-10: 1394319959
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Cabianca, Dario
Hersteller: Wiley
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 234 x 187 x 35 mm
Von/Mit: Dario Cabianca
Erscheinungsdatum: 09.07.2025
Gewicht: 0,738 kg
Artikel-ID: 133346528
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