Amazon SageMaker Best Practices Proven Tips and Tricks to Build Successful Machine Learning Solutions on Amazon SageMaker.

Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of Amazon SageMaker for developing and integrating ML models into production Key Features Learn best practices for all phases of building machine learning solutions - from data preparation to monitoring m...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Muppala, Sireesha
Otros Autores: DeFauw, Randy, Eigenbrode, Shelbee
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2021.
Temas:
Amazon Web Services (Firm)
Machine learning.
Cloud computing.
Apprentissage automatique.
Infonuagique.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Dedication
  • Contributors
  • Table of Contents
  • Preface
  • Section 1: Processing Data at Scale
  • Chapter 1: Amazon SageMaker Overview
  • Technical requirements
  • Preparing, building, training and tuning, deploying, and managing ML models
  • Discussion of data preparation capabilities
  • SageMaker Ground Truth
  • SageMaker Data Wrangler
  • SageMaker Processing
  • SageMaker Feature Store
  • SageMaker Clarify
  • Feature tour of model-building capabilities
  • SageMaker Studio
  • SageMaker notebook instances
  • SageMaker algorithms
  • BYO algorithms and scripts
  • Feature tour of training and tuning capabilities
  • SageMaker training jobs
  • Autopilot
  • HPO
  • SageMaker Debugger
  • SageMaker Experiments
  • Feature tour of model management and deployment capabilities
  • Model Monitor
  • Model endpoints
  • Edge Manager
  • Summary
  • Chapter 2: Data Science Environments
  • Technical requirements
  • Machine learning use case and dataset
  • Creating data science environments
  • Creating repeatability through IaC/CaC
  • Amazon SageMaker notebook instances
  • Amazon SageMaker Studio
  • Providing and creating data science environments as IT services
  • Creating a portfolio in AWS Service Catalog
  • Amazon SageMaker notebook instances
  • Amazon SageMaker Studio
  • Summary
  • References
  • Chapter 3: Data Labeling with Amazon SageMaker Ground Truth
  • Technical requirements
  • Challenges with labeling data at scale
  • Addressing unique labeling requirements with custom labeling workflows
  • A private labeling workforce
  • Listing the data to label
  • Creating the workflow
  • Improving labeling quality using multiple workers
  • Using active learning to reduce labeling time
  • Security and permissions
  • Summary
  • Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
  • Technical requirements
  • Visual data preparation with Data Wrangler
  • Bias detection and explainability with Data Wrangler and Clarify
  • Data preparation at scale with SageMaker Processing
  • Loading the dataset
  • Drop columns
  • Converting data types
  • Scaling numeric fields
  • Featurizing the date
  • Simulating labels for air quality
  • Encoding categorical variables
  • Splitting and saving the dataset
  • Summary
  • Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store
  • Technical requirements
  • Amazon SageMaker Feature Store essentials
  • Creating feature groups
  • Populating feature groups
  • Retrieving features from feature groups
  • Creating reusable features to reduce feature inconsistencies and inference latency
  • Designing solutions for near real-time ML predictions
  • Summary
  • References
  • Section 2: Model Training Challenges
  • Chapter 6: Training and Tuning at Scale
  • Technical requirements
  • ML training at scale with SageMaker distributed libraries

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