Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

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
	Understanding bias, fairness in ML, and ML explainability
	Detecting bias in ML
		Detecting pretraining bias
		Mitigating bias and training a model
		Detecting post-training bias
	Explaining ML models using SHAP values
	Summary
Chapter 7: Hosting ML Models in the Cloud: Best Practices
	Technical requirements
	Deploying models in the cloud after training
	Inferencing in batches with batch transform
	Hosting real-time endpoints
	Optimizing your model deployment
		Hosting multi-model endpoints to save costs
		Optimizing instance type and autoscaling with load testing
	Summary
Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot
	Technical requirements
	Launching a SageMaker JumpStart solution
		Solution catalog for industries
		Deploying the Product Defect Detection solution
	SageMaker JumpStart model zoo
		Model collection
		Deploying a model
		Fine-tuning a model
	Creating a high-quality model with SageMaker Autopilot
		Wine quality prediction
		Setting up an Autopilot job
		Understanding an Autopilot job
		Evaluating Autopilot models
	Summary
	Further reading
Part 3 – The Production and Operation of Machine Learning with SageMaker Studio
Chapter 9: Training ML Models at Scale in SageMaker Studio
	Technical requirements
	Performing distributed training in SageMaker Studio
		Understanding the concept of distributed training
		The data parallel library with TensorFlow
		Model parallelism with PyTorch
	Monitoring model training and compute resources with SageMaker Debugger
	Managing long-running jobs with checkpointing and spot training
	Summary
Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor
	Technical requirements
	Understanding drift in ML
	Monitoring data and performance drift in SageMaker Studio
		Training and hosting a model
		Creating inference traffic and ground truth
		Creating a data quality monitor
		Creating a model quality monitor
	Reviewing model monitoring results in SageMaker Studio
	Summary
Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry
	Technical requirements
	Understanding ML operations and CI/CD
	Creating a SageMaker project
	Orchestrating an ML pipeline with SageMaker Pipelines
	Running CI/CD in SageMaker Studio
	Summary
Index
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