Machine Learning with Amazon SageMaker Cookbook: 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Length: 762 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2021-10-29
ISBN-10: 1800567030
ISBN-13: 9781800567030
Sales Rank: #1428018 (See Top 100 Books)

A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMaker

Key Features

Perform ML experiments with built-in and custom algorithms in SageMaker
Explore proven solutions when working with TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learn
Use the different features and capabilities of SageMaker to automate relevant ML processes

Book Description

Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you’ll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.

This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You’ll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You’ll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You’ll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you’ll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.

By the end of this book, you’ll be able to combine the different solutions you’ve learned as building blocks to solve real-world ML problems.

What you will learn

Train and deploy NLP, time series forecasting, and computer vision models to solve different business problems
Push the limits of customization in SageMaker using custom container images
Use AutoML capabilities with SageMaker Autopilot to create high-quality models
Work with effective data analysis and preparation techniques
Explore solutions for debugging and managing ML experiments and deployments
Deal with bias detection and ML explainability requirements using SageMaker Clarify
Automate intermediate and complex deployments and workflows using a variety of solutions

Who this book is for

This book is for developers, data scientists, and machine learning practitioners interested in using Amazon SageMaker to build, analyze, and deploy machine learning models with 80 step-by-step recipes. All you need is an AWS account to get things running. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.

Getting Started with Machine Learning Using Amazon SageMaker
Building and Using your own Algorithm Container Image
Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker
Preparing, Processing, and Analyzing the Data
Effectively Managing Machine Learning Experiments
Automated Machine Learning in Amazon SageMaker
Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor
Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms
Managing Machine Learning Workflows and Deployments

Machine Learning with Amazon SageMaker Cookbook
Contributors
About the author
About the reviewers
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Code in Action
    Download the color images
    Conventions used
    Sections
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
    Get in touch
    Share Your Thoughts
Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker
    Technical requirements
    Launching an Amazon SageMaker Notebook Instance
        Getting ready
        How to do it…
        How it works…
    Checking the versions of the SageMaker Python SDK and the AWS CLI
        Getting ready
        How to do it…
        How it works…
    Preparing the Amazon S3 bucket and the training dataset for the linear regression experiment
        Getting ready
        How to do it…
        How it works…
    Visualizing and understanding your data in Python
        Getting ready
        How to do it…
        How it works…
    Training your first model in Python
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
    Loading a linear learner model with Apache MXNet in Python
        Getting ready
        How to do it…
        How it works…
    Evaluating the model in Python
        Getting ready
        How to do it…
        How it works…
        There's more…
    Deploying your first model in Python
        Getting ready
        How to do it…
        How it works…
    Invoking an Amazon SageMaker model endpoint with the SageMakerRuntime client from boto3
        Getting ready
        How to do it…
        How it works…
Chapter 2: Building and Using Your Own Algorithm Container Image
    Technical requirements
    Launching and preparing the Cloud9 environment
        Getting ready
        How to do it…
        How it works…
    Setting up the Python and R experimentation environments
        Getting ready
        How to do it…
        How it works…
        There's more…
    Preparing and testing the train script in Python
        Getting ready
        How to do it…
        How it works…
        There's more…
    Preparing and testing the serve script in Python
        Getting ready
        How to do it…
        How it works…
    Building and testing the custom Python algorithm container image
        Getting ready
        How to do it…
        How it works…
    Pushing the custom Python algorithm container image to an Amazon ECR repository
        Getting ready
        How to do it…
        How it works…
    Using the custom Python algorithm container image for training and inference with Amazon SageMaker Local Mode
        Getting ready
        How to do it…
        How it works…
    Preparing and testing the train script in R 
        Getting ready
        How to do it...
        How it works…
        There's more…
    Preparing and testing the serve script in R
        Getting ready
        How to do it...
        How it works…
    Building and testing the custom R algorithm container image
        Getting ready
        How to do it...
        How it works…
    Pushing the custom R algorithm container image to an Amazon ECR repository
        Getting ready
        How to do it...
        How it works…
    Using the custom R algorithm container image for training and inference with Amazon SageMaker Local Mode
        Getting ready
        How to do it...
        How it works…
Chapter 3: Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker
    Technical requirements
    Preparing the SageMaker notebook instance for multiple deep learning local experiments
        Getting ready
        How to do it
        How it works...
        There's more...
    Generating a synthetic dataset for deep learning experiments
        Getting ready
        How to do it
        How it works...
    Preparing the entrypoint TensorFlow and Keras training script
        Getting ready
        How to do it
        How it works...
        There's more...
    Training and deploying a TensorFlow and Keras model with the SageMaker Python SDK
        Getting ready
        How to do it
        How it works...
        There's more...
        See also
    Preparing the entrypoint PyTorch training script
        Getting ready
        How to do it
        How it works...
    Preparing the entrypoint PyTorch inference script
        Getting ready
        How to do it
        How it works...
    Training and deploying a PyTorch model with the SageMaker Python SDK
        Getting ready
        How to do it
        How it works...
        See also
    Preparing the entrypoint scikit-learn training script
        Getting ready
        How to do it
        How it works...
    Training and deploying a scikit-learn model with the SageMaker Python SDK
        Getting ready
        How to do it
        How it works...
        See also
    Debugging disk space issues when using local mode
        Getting ready
        How to do it
        How it works...
        There's more...
    Debugging container execution issues when using local mode
        Getting ready
        How to do it...
        How it works...
        There's more...
Chapter 4: Preparing, Processing, and Analyzing the Data
    Technical requirements
    Generating a synthetic dataset for anomaly detection experiments
        Getting ready
        How to do it…
        How it works…
    Training and deploying an RCF model
        Getting ready…
        How to do it…
        How it works…
        See also
    Invoking machine learning models with Amazon Athena using SQL queries
        Getting ready
        How to do it…
        How it works…
    Analyzing data with Amazon Athena in Python
        Getting ready
        How to do it…
        How it works…
    Generating a synthetic dataset for analysis and transformation
        Getting ready
        How to do it…
        How it works…
    Performing dimensionality reduction with the built-in PCA algorithm
        Getting ready
        How to do it…
        How it works…
        See also
    Performing cluster analysis with the built-in KMeans algorithm
        Getting ready
        How to do it…
        How it works…
        See also
    Converting CSV data into protobuf recordIO format
        Getting ready
        How to do it…
        How it works…
    Training a KNN model using the protobuf recordIO training input type
        Getting ready
        How to do it…
        How it works…
    Preparing the SageMaker Processing prerequisites using the AWS CLI
        Getting ready
        How to do it…
        How it works…
    Managed data processing with SageMaker Processing in Python
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
    Managed data processing with SageMaker Processing in R
        Getting ready
        How to do it…
        How it works…
Chapter 5: Effectively Managing Machine Learning Experiments
    Technical requirements
    Synthetic data generation for classification problems
        Getting ready
        How to do it…
        How it works…
        There's more…
    Identifying issues with SageMaker Debugger
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
    Inspecting SageMaker Debugger logs and results
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
    Running and managing multiple experiments with SageMaker Experiments
        Getting ready
        How to do it…
        How it works…
        There's more…
    Experiment analytics with SageMaker Experiments
        Getting ready
        How to do it…
        How it works…
    Inspecting experiments, trials, and trial components with SageMaker Experiments
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
Chapter 6: Automated Machine Learning in Amazon SageMaker
    Technical requirements
    Onboarding to SageMaker Studio
        Getting ready
        How to do it…
        How it works…
        There's more…
    Generating a synthetic dataset with additional columns containing random values
        Getting ready
        How to do it…
        How it works…
        There's more…
    Creating and monitoring a SageMaker Autopilot experiment in SageMaker Studio (console)
        Getting ready
        How to do it…
        How it works…
    Creating and monitoring a SageMaker Autopilot experiment using the SageMaker Python SDK
        Getting ready
        How to do it…
        How it works…
        There's more…
    Inspecting the SageMaker Autopilot experiment's results and artifacts
        Getting ready
        How to do it…
        How it works…
    Performing Automatic Model Tuning with the SageMaker XGBoost built-in algorithm
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
    Analyzing the Automatic Model Tuning job results
        Getting ready
        How to do it…
        How it works…
Chapter 7: Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor
    Technical requirements
    Generating a synthetic dataset and using SageMaker Feature Store for storage and management
        Getting ready
        How to do it…
        How it works…
    Querying data from the offline store of SageMaker Feature Store and uploading it to Amazon S3
        Getting ready
        How to do it…
        How it works…
        There's more…
    Detecting pre-training bias with SageMaker Clarify
        Getting ready
        How to do it…
        How it works…
        There's more…
    Detecting post-training bias with SageMaker Clarify
        Getting ready
        How to do it…
        How it works…
    Enabling ML explainability with SageMaker Clarify
        Getting ready
        How to do it…
        How it works…
    Deploying an endpoint from a model and enabling data capture with SageMaker Model Monitor
        Getting ready
        How to do it…
        How it works…
    Baselining and scheduled monitoring with SageMaker Model Monitor
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
Chapter 8: Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms
    Technical requirements
    Generating a synthetic dataset for text classification problems
        Getting ready
        How to do it…
        How it works…
        There's more…
    Preparing the test dataset for batch transform inference jobs
        Getting ready
        How to do it…
        How it works…
    Training and deploying a BlazingText model
        Getting ready
        How to do it…
        How it works…
        See more
    Using Batch Transform for inference
        Getting ready
        How to do it…
        How it works…
        See also
    Preparing the datasets for image classification using the Apache MXNet Vision Datasets classes
        Getting ready
        How to do it…
        How it works…
        See also
    Training and deploying an image classifier using the built-in Image Classification Algorithm in SageMaker
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
    Generating a synthetic time series dataset
        Getting ready
        How to do it…
        How it works…
    Performing the train-test split on a time series dataset
        Getting ready
        How to do it…
        How it works…
    Training and deploying a DeepAR model
        Getting ready
        How to do it…
        How it works…
    Performing probabilistic forecasting with a deployed DeepAR model
        Getting ready
        How to do it...
        How it works…
        See also
Chapter 9: Managing Machine Learning Workflows and Deployments
    Technical requirements
    Working with Hugging Face models
        Getting ready
        How to do it…
        How it works…
        There's more…
        See also
    Preparing the prerequisites of a multi-model endpoint deployment
        Getting ready
        How to do it…
        How it works…
    Hosting multiple models with multi-model endpoints
        Getting ready
        How to do it…
        How it works…
    Setting up A/B testing on multiple models with production variants
        Getting ready
        How to do it…
        How it works…
        There's more…
    Preparing the Step Functions execution role
        Getting ready
        How to do it…
        How it works…
    Managing ML workflows with AWS Step Functions and the Data Science SDK
        Getting ready
        How to do it…
        How it works…
        See also
    Managing ML workflows with SageMaker Pipelines
        Getting ready
        How to do it…
        How it works…
        See also
    Why subscribe?
Other Books You May Enjoy
    Packt is searching for authors like you
    Share Your Thoughts

AI & Machine Learning Data Modeling & Design Machine Theory Neural Networks

Donate to keep this site alive

To access the Link, solve the captcha.

How to download source code?

1. Go to: https://github.com/PacktPublishing

2. In the Find a repository… box, search the book title: Machine Learning with Amazon SageMaker Cookbook: 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments, sometime you may not get the results, please search the main title.