Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for developers and data scientists

Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Section 1: Introduction to Amazon SageMaker
Chapter 1: Introducing Amazon SageMaker
	Technical requirements
	Exploring the capabilities of Amazon SageMaker
		The main capabilities of Amazon SageMaker
		The Amazon SageMaker API
	Setting up Amazon SageMaker on your local machine
		Installing the SageMaker SDK with virtualenv
		Installing the SageMaker SDK with Anaconda
		A word about AWS permissions
	Setting up Amazon SageMaker Studio
		Onboarding to Amazon SageMaker Studio
		Onboarding with the quick start procedure
	Deploying one-click solutions and models with Amazon SageMaker JumpStart
		Deploying a solution
		Deploying a model
		Fine-tuning a model
	Summary
Chapter 2: Handling Data Preparation Techniques
	Technical requirements
	Labeling data with Amazon SageMaker Ground Truth
		Using workforces
		Creating a private workforce
		Uploading data for labeling
		Creating a labeling job
		Labeling images
		Labeling text
	Transforming data with Amazon SageMaker Data Wrangler
		Loading a dataset in SageMaker Data Wrangler
		Transforming a dataset in SageMaker Data Wrangler
		Exporting a SageMaker Data Wrangler pipeline
	Running batch jobs with Amazon SageMaker Processing
		Discovering the Amazon SageMaker Processing API
		Processing a dataset with scikit-learn
		Processing a dataset with your own code
	Summary
Section 2: Building and Training Models
Chapter 3: AutoML with Amazon SageMaker Autopilot
	Technical requirements
	Discovering Amazon SageMaker Autopilot
		Analyzing data
		Feature engineering
		Model tuning
	Using Amazon SageMaker Autopilot in SageMaker Studio
		Launching a job
		Monitoring a job
		Comparing jobs
		Deploying and invoking a model
	Using the SageMaker Autopilot SDK
		Launching a job
		Monitoring a job
		Cleaning up
	Diving deep on SageMaker Autopilot
		The job artifacts
		The data exploration notebook
		The candidate generation notebook
	Summary
Chapter 4: Training Machine Learning Models
	Technical requirements
	Discovering the built-in algorithms in Amazon SageMaker
		Supervised learning
		Unsupervised learning
		A word about scalability
	Training and deploying models with built-in algorithms
		Understanding the end-to-end workflow
		Using alternative workflows
		Using fully managed infrastructure
	Using the SageMaker SDK with built-in algorithms
		Preparing data
		Configuring a training job
		Launching a training job
		Deploying a model
		Cleaning up
	Working with more built-in algorithms
		Regression with XGBoost
		Recommendation with Factorization Machines
		Using Principal Component Analysis
		Detecting anomalies with Random Cut Forest
	Summary
Chapter 5: Training CV Models
	Technical requirements
	Discovering the CV built-in algorithms in Amazon SageMaker
		Discovering the image classification algorithm
		Discovering the object detection algorithm
		Discovering the semantic segmentation algorithm
		Training with CV algorithms
	Preparing image datasets
		Working with image files
		Working with RecordIO files
		Working with SageMaker Ground Truth files
	Using the built-in CV algorithms
		Training an image classification model
		Fine-tuning an image classification model
		Training an object detection model
		Training a semantic segmentation model
	Summary
Chapter 6: Training Natural Language Processing Models
	Technical requirements
	Discovering the NLP built-in algorithms in Amazon SageMaker
		Discovering the BlazingText algorithm
		Discovering the LDA algorithm
		Discovering the NTM algorithm
		Discovering the seq2sea algorithm
		Training with NLP algorithms
	Preparing natural language datasets
		Preparing data for classification with BlazingText
		Preparing data for classification with BlazingText, version 2
		Preparing data for word vectors with BlazingText
		Preparing data for topic modeling with LDA and NTM
		Using datasets labeled with SageMaker Ground Truth
	Using the built-in algorithms for NLP
		Classifying text with BlazingText
		Computing word vectors with BlazingText
		Using BlazingText models with FastText
		Modeling topics with LDA
		Modeling topics with NTM
	Summary
Chapter 7: Extending Machine Learning Services Using Built-In Frameworks
	Technical requirements
	Discovering the built-in frameworks in Amazon SageMaker
		Running a first example with XGBoost
		Working with framework containers
		Training and deploying locally
		Training with script mode
		Understanding model deployment
		Managing dependencies
		Putting it all together
	Running your framework code on Amazon SageMaker
	Using the built-in frameworks
		Working with TensorFlow and Keras
		Working with PyTorch
		Working with Hugging Face
		Working with Apache Spark
	Summary
Chapter 8: Using Your Algorithms and Code
	Technical requirements
	Understanding how SageMaker invokes your code
	Customizing an existing framework container
		Setting up your build environment on EC2
		Building training and inference containers
	Using the SageMaker Training Toolkit with scikit-learn
	Building a fully custom container for scikit-learn
		Training with a fully custom container
		Deploying a fully custom container
	Building a fully custom container for R
		Coding with R and plumber
		Building a custom container
		Training and deploying a custom container on SageMaker
	Training and deploying with your own code on MLflow
		Installing MLflow
		Training a model with MLflow
		Building a SageMaker container with MLflow
	Building a fully custom container for SageMaker Processing
	Summary
Section 3: Diving Deeper into Training
Chapter 9: Scaling Your Training Jobs
	Technical requirements
	Understanding when and how to scale
		Understanding what scaling means
		Adapting training time to business requirements
		Right-sizing training infrastructure
		Deciding when to scale
		Deciding how to scale
		Scaling a BlazingText training job
	Monitoring and profiling training jobs with Amazon SageMaker Debugger
		Viewing monitoring and profiling information in SageMaker Studio
		Enabling profiling in SageMaker Debugger
		Solving training challenges
	Streaming datasets with pipe mode
		Using pipe mode with built-in algorithms
		Using pipe mode with other algorithms and frameworks
		Simplifying data loading with MLIO
		Training factorization machines with pipe mode
	Distributing training jobs
		Understanding data parallelism and model parallelism
		Distributing training for built-in algorithms
		Distributing training for built-in frameworks
		Distributing training for custom containers
	Scaling an image classification model on ImageNet
		Preparing the ImageNet dataset
		Defining our training job
		Training on ImageNet
		Updating batch size
		Adding more instances
		Summing things up
	Training with the SageMaker data and model parallel libraries
		Training on TensorFlow with SageMaker DDP
		Training on Hugging Face with SageMaker DDP
		Training on Hugging Face with SageMaker DMP
	Using other storage services
		Working with SageMaker and Amazon EFS
		Working with SageMaker and Amazon FSx for Lustre
	Summary
Chapter 10: Advanced Training Techniques
	Technical requirements
	Optimizing training costs with managed spot training
		Comparing costs
		Understanding Amazon EC2 Spot Instances
		Understanding managed spot training
		Using managed spot training with object detection
		Using managed spot training and checkpointing with Keras
	Optimizing hyperparameters with automatic model tuning
		Understanding automatic model tuning
		Using automatic model tuning with object detection
		Using automatic model tuning with Keras
		Using automatic model tuning for architecture search
	Exploring models with SageMaker Debugger
		Debugging an XGBoost job
		Inspecting an XGBoost job
		Debugging and inspecting a Keras job
	Managing features and building datasets with SageMaker Feature Store
		Engineering features with SageMaker Processing
		Creating a feature group
		Ingesting features
		Querying features to build a dataset
		Exploring other capabilities of SageMaker Feature Store
	Detecting bias in datasets and explaining predictions with SageMaker Clarify
		Configuring a bias analysis with SageMaker Clarify
		Running a bias analysis
		Analyzing bias metrics
		Running an explainability analysis
		Mitigating bias
	Summary
Section 4: Managing Models in Production
Chapter 11: Deploying Machine Learning Models
	Technical requirements
	Examining model artifacts and exporting models
		Examining and exporting built-in models
		Examining and exporting built-in CV models
		Examining and exporting XGBoost models
		Examining and exporting scikit-learn models
		Examining and exporting TensorFlow models
		Examining and exporting Hugging Face models
	Deploying models on real-time endpoints
		Managing endpoints with the SageMaker SDK
		Managing endpoints with the boto3 SDK
	Deploying models on batch transformers
	Deploying models on inference pipelines
	Monitoring prediction quality with Amazon SageMaker Model Monitor
		Capturing data
		Creating a baseline
		Setting up a monitoring schedule
		Sending bad data
		Examining violation reports
	Deploying models to container services
		Training on SageMaker and deploying on Amazon Fargate
	Summary
Chapter 12: Automating Machine Learning Workflows
	Technical requirements
	Automating with AWS CloudFormation
		Writing a template
		Deploying a model to a real-time endpoint
		Modifying a stack with a change set
		Adding a second production variant to the endpoint
		Implementing canary deployment
		Implementing blue-green deployment
	Automating with AWS CDK
		Installing the CDK
		Creating a CDK application
		Writing a CDK application
		Deploying a CDK application
	Building end-to-end workflows with AWS Step Functions
		Setting up permissions
		Implementing our first workflow
		Adding parallel execution to a workflow
		Adding a Lambda function to a workflow
	Building end-to-end workflows with Amazon SageMaker Pipelines
		Defining workflow parameters
		Processing the dataset with SageMaker Processing
		Ingesting the dataset in SageMaker Feature Store with SageMaker Processing
		Building a dataset with Amazon Athena and SageMaker Processing
		Training a model
		Creating and registering a model in SageMaker Pipelines
		Creating a pipeline
		Running a pipeline
		Deploying a model from the model registry
	Summary
Chapter 13: Optimizing Prediction Cost and Performance
	Technical requirements
	Autoscaling an endpoint
	Deploying a multi-model endpoint
		Understanding multi-model endpoints
		Building a multi-model endpoint with scikit-learn
	Deploying a model with Amazon Elastic Inference
		Deploying a model with Amazon Elastic Inference
	Compiling models with Amazon SageMaker Neo
		Understanding Amazon SageMaker Neo
		Compiling and deploying an image classification model on SageMaker
		Exploring models compiled with Neo
		Deploying an image classification model on a Raspberry Pi
		Deploying models on AWS Inferentia
	Building a cost optimization checklist
		Optimizing costs for data preparation
		Optimizing costs for experimentation
		Optimizing costs for model training
		Optimizing costs for model deployment
	Summary
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