Getting started with Amazon SageMaker Studio : learn to build end-to-end machine learning projects in the SageMaker machine learning IDE /

Build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code Key Features Understand the ML lifecycle in the cloud and its development on Amazon SageMaker Studio Learn to app...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Hsieh, Michael (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2022.
Temas:
Amazon Web Services (Firm)
Amazon SageMaker Studio.
Machine learning.
Cloud computing.
Apprentissage automatique.
Infonuagique.
Cloud computing
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Contributors
  • Table of Contents
  • Preface
  • Part 1
  • Introduction to Machine Learning on Amazon SageMaker Studio
  • Chapter 1: Machine Learning and Its Life Cycle in the Cloud
  • Technical requirements
  • Understanding ML and its life cycle
  • An ML life cycle
  • Building ML in the cloud
  • Exploring AWS essentials for ML
  • Compute
  • Storage
  • Database and analytics
  • Security
  • Setting up an AWS environment
  • Summary
  • Chapter 2: Introducing Amazon SageMaker Studio
  • Technical requirements
  • Introducing SageMaker Studio and its components
  • Prepare
  • Build
  • Training and tuning
  • Deploy
  • MLOps
  • Setting up SageMaker Studio
  • Setting up a domain
  • Walking through the SageMaker Studio UI
  • The main work area
  • The sidebar
  • Hello world!"" in SageMaker Studio
  • Demystifying SageMaker Studio notebooks, instances, and kernels
  • Using the SageMaker Python SDK
  • Summary
  • Part 2
  • End-to-End Machine Learning Life Cycle with SageMaker Studio
  • Chapter 3: Data Preparation with SageMaker Data Wrangler
  • Technical requirements
  • Getting started with SageMaker Data Wrangler for customer churn prediction
  • Preparing the use case
  • Launching SageMaker Data Wrangler
  • Importing data from sources
  • Importing from S3
  • Importing from Athena
  • Editing the data type
  • Joining tables
  • Exploring data with visualization
  • Understanding the frequency distribution with a histogram
  • Scatter plots
  • Previewing ML model performance with Quick Model
  • Revealing target leakage
  • Creating custom visualizations
  • Applying transformation
  • Exploring performance while wrangling
  • Exporting data for ML training
  • Summary
  • Chapter 4: Building a Feature Repository with SageMaker Feature Store
  • Technical requirements
  • Understanding the concept of a feature store
  • Understanding an online store
  • Understanding an offline store
  • Getting started with SageMaker Feature Store
  • Creating a feature group
  • Ingesting data to SageMaker Feature Store
  • Ingesting from SageMaker Data Wrangler
  • Accessing features from SageMaker Feature Store
  • Accessing a feature group in the Studio UI
  • Accessing an offline store
  • building a dataset for analysis and training
  • Accessing online store
  • low-latency feature retrieval
  • Summary
  • Chapter 5: Building and Training ML Models with SageMaker Studio IDE
  • Technical requirements
  • Training models with SageMaker's built-in algorithms
  • Training an NLP model easily
  • Managing training jobs with SageMaker Experiments
  • Training with code written in popular frameworks
  • TensorFlow
  • PyTorch
  • Hugging Face
  • MXNet
  • Scikit-learn
  • Developing and collaborating using SageMaker Notebook
  • Summary
  • Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify
  • Technical requirements

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