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…mehr
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 www.wiley.com/ 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
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.
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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
