AWS Certified Machine Learning Study Guide specialty (mls-c01) exam
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...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Incorporated
[2022]
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009645683806719 |
Tabla de Contenidos:
- 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
- Summary
- Exam Essentials
- Review Questions
- Part II Phases of Machine Learning Workloads
- Chapter 3 Business Understanding
- Phases of ML Workloads
- Business Problem Identification
- Summary
- Exam Essentials
- Review Questions
- Chapter 4 Framing a Machine Learning Problem
- ML Problem Framing
- Recommended Practices
- Summary
- Exam Essentials
- Review Questions
- Chapter 5 Data Collection
- Basic Data Concepts
- Data Repositories
- Data Migration to AWS
- Batch Data Collection
- Streaming Data Collection
- Summary
- Exam Essentials
- Review Questions
- Chapter 6 Data Preparation
- Data Preparation Tools.
- SageMaker Ground Truth
- Amazon EMR
- Amazon SageMaker Processing
- AWS Glue
- Amazon Athena
- Redshift Spectrum
- Summary
- Exam Essentials
- Review Questions
- 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
- Summary
- Exam Essentials
- Review Questions
- 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
- Summary
- Exam Essentials
- Review Questions
- Chapter 9 Model Evaluation
- Experiment Management
- Metrics and Visualization
- Metrics in AWS AI/ML Services
- Summary
- Exam Essentials
- Review Questions
- 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
- Summary
- Exam Essentials
- Review Questions
- Chapter 11 Application Integration
- Integration with On-Premises Systems
- Integration with Cloud Systems
- Integration with Front-End Systems
- Summary
- Exam Essentials
- Review Questions
- 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
- Summary
- Exam Essentials
- Review Questions
- 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
- Data Protection
- Network Isolation
- Logging and Monitoring
- Compliance Scope
- AI Services Security
- Summary
- Exam Essentials
- Review Questions
- Chapter 14 Reliability Pillar
- Reliability on AWS
- Change Management for ML
- Failure Management for ML
- Summary
- Exam Essentials
- Review Questions
- Chapter 15 Performance Efficiency Pillar for ML
- Performance Efficiency for ML on AWS
- Selection
- Review
- Monitoring
- Trade-offs
- Summary
- Exam Essentials
- Review Questions
- Chapter 16 Cost Optimization Pillar for ML
- Common Design Principles
- Cost Optimization for ML Workloads
- Design Principles
- Common Cost Optimization Strategies
- Summary
- Exam Essentials
- Review Questions
- 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
- Summary
- Exam Essentials
- 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.
