Getting started with Amazon SageMaker Studio : learn to build end-to-end machine learning projects in the SageMaker machine learning IDE / Michael Hsieh.
2022
Q325.5
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Details
Title
Getting started with Amazon SageMaker Studio : learn to build end-to-end machine learning projects in the SageMaker machine learning IDE / Michael Hsieh.
Author
ISBN
1801073481
9781801073486 (electronic bk.)
9781801070157 (pbk.)
9781801073486 (electronic bk.)
9781801070157 (pbk.)
Published
Birmingham : Packt Publishing, 2022.
Language
English
Description
1 online resource
Call Number
Q325.5
System Control No.
(OCoLC)1308478143
Summary
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 apply SageMaker features in SageMaker Studio for ML use cases Scale and operationalize the ML lifecycle effectively using SageMaker Studio Book Description Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment. In this book, you'll start by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio. By the end of this book, you'll have learned ML best practices regarding Amazon SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases. What you will learn Explore the ML development life cycle in the cloud Understand SageMaker Studio features and the user interface Build a dataset with clicks and host a feature store for ML Train ML models with ease and scale Create ML models and solutions with little code Host ML models in the cloud with optimal cloud resources Ensure optimal model performance with model monitoring Apply governance and operational excellence to ML projects Who this book is for This book is for data scientists and machine learning engineers who are looking to become well-versed with Amazon SageMaker Studio and gain hands-on machine learning experience to handle every step in the ML lifecycle, including building data as well as training and hosting models. Although basic knowledge of machine learning and data science is necessary, no previous knowledge of SageMaker Studio and cloud experience is required.
Formatted Contents Note
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
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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