book

Machine Learning with Amazon SageMaker Cookbook

Name: Machine Learning with Amazon SageMaker Cookbook
Author: Joshua Arvin Lat
ISBN: 9781800567030

by Joshua Arvin Lat

October 2021

Intermediate to advanced

762 pages

14h 21m

English

Packt Publishing

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Machine Learning with Amazon SageMaker Cookbook
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesCode in ActionDownload the color imagesConventions usedSectionsGetting readyHow to do it…How it works…There's more…See alsoGet in touchShare Your Thoughts
Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker
Technical requirementsLaunching an Amazon SageMaker Notebook InstanceGetting readyHow to do it…How it works…Checking the versions of the SageMaker Python SDK and the AWS CLIGetting readyHow to do it…How it works…Preparing the Amazon S3 bucket and the training dataset for the linear regression experimentGetting readyHow to do it…How it works…Visualizing and understanding your data in PythonGetting readyHow to do it…How it works…Training your first model in PythonGetting readyHow to do it…How it works…There's more…See alsoLoading a linear learner model with Apache MXNet in PythonGetting readyHow to do it…How it works…Evaluating the model in PythonGetting readyHow to do it…How it works…There's more…Deploying your first model in PythonGetting readyHow to do it…How it works…Invoking an Amazon SageMaker model endpoint with the SageMakerRuntime client from boto3Getting readyHow to do it…How it works…
Chapter 2: Building and Using Your Own Algorithm Container Image
Technical requirementsLaunching and preparing the Cloud9 environmentGetting readyHow to do it…How it works…Setting up the Python and R experimentation environmentsGetting readyHow to do it…How it works…There's more…Preparing and testing the train script in PythonGetting readyHow to do it…How it works…There's more…Preparing and testing the serve script in PythonGetting readyHow to do it…How it works…Building and testing the custom Python algorithm container imageGetting readyHow to do it…How it works…Pushing the custom Python algorithm container image to an Amazon ECR repositoryGetting readyHow to do it…How it works…Using the custom Python algorithm container image for training and inference with Amazon SageMaker Local ModeGetting readyHow to do it…How it works…Preparing and testing the train script in R Getting readyHow to do it...How it works…There's more…Preparing and testing the serve script in RGetting readyHow to do it...How it works…Building and testing the custom R algorithm container imageGetting readyHow to do it...How it works…Pushing the custom R algorithm container image to an Amazon ECR repositoryGetting readyHow to do it...How it works…Using the custom R algorithm container image for training and inference with Amazon SageMaker Local ModeGetting readyHow to do it...How it works…
Chapter 3: Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker
Technical requirementsPreparing the SageMaker notebook instance for multiple deep learning local experimentsGetting readyHow to do itHow it works...There's more...Generating a synthetic dataset for deep learning experimentsGetting readyHow to do itHow it works...Preparing the entrypoint TensorFlow and Keras training scriptGetting readyHow to do itHow it works...There's more...Training and deploying a TensorFlow and Keras model with the SageMaker Python SDKGetting readyHow to do itHow it works...There's more...See alsoPreparing the entrypoint PyTorch training scriptGetting readyHow to do itHow it works...Preparing the entrypoint PyTorch inference scriptGetting readyHow to do itHow it works...Training and deploying a PyTorch model with the SageMaker Python SDKGetting readyHow to do itHow it works...See alsoPreparing the entrypoint scikit-learn training scriptGetting readyHow to do itHow it works...Training and deploying a scikit-learn model with the SageMaker Python SDKGetting readyHow to do itHow it works...See alsoDebugging disk space issues when using local modeGetting readyHow to do itHow it works...There's more...Debugging container execution issues when using local modeGetting readyHow to do it...How it works...There's more...
Chapter 4: Preparing, Processing, and Analyzing the Data
Technical requirementsGenerating a synthetic dataset for anomaly detection experimentsGetting readyHow to do it…How it works…Training and deploying an RCF modelGetting ready…How to do it…How it works…See alsoInvoking machine learning models with Amazon Athena using SQL queriesGetting readyHow to do it…How it works…Analyzing data with Amazon Athena in PythonGetting readyHow to do it…How it works…Generating a synthetic dataset for analysis and transformationGetting readyHow to do it…How it works…Performing dimensionality reduction with the built-in PCA algorithmGetting readyHow to do it…How it works…See alsoPerforming cluster analysis with the built-in KMeans algorithmGetting readyHow to do it…How it works…See alsoConverting CSV data into protobuf recordIO formatGetting readyHow to do it…How it works…Training a KNN model using the protobuf recordIO training input typeGetting readyHow to do it…How it works…Preparing the SageMaker Processing prerequisites using the AWS CLIGetting readyHow to do it…How it works…Managed data processing with SageMaker Processing in PythonGetting readyHow to do it…How it works…There's more…See alsoManaged data processing with SageMaker Processing in RGetting readyHow to do it…How it works…
Chapter 5: Effectively Managing Machine Learning Experiments
Technical requirementsSynthetic data generation for classification problemsGetting readyHow to do it…How it works…There's more…Identifying issues with SageMaker DebuggerGetting readyHow to do it…How it works…There's more…See alsoInspecting SageMaker Debugger logs and resultsGetting readyHow to do it…How it works…There's more…See alsoRunning and managing multiple experiments with SageMaker ExperimentsGetting readyHow to do it…How it works…There's more…Experiment analytics with SageMaker ExperimentsGetting readyHow to do it…How it works…Inspecting experiments, trials, and trial components with SageMaker ExperimentsGetting readyHow to do it…How it works…There's more…See also
Chapter 6: Automated Machine Learning in Amazon SageMaker
Technical requirementsOnboarding to SageMaker StudioGetting readyHow to do it…How it works…There's more…Generating a synthetic dataset with additional columns containing random valuesGetting readyHow to do it…How it works…There's more…Creating and monitoring a SageMaker Autopilot experiment in SageMaker Studio (console)Getting readyHow to do it…How it works…Creating and monitoring a SageMaker Autopilot experiment using the SageMaker Python SDKGetting readyHow to do it…How it works…There's more…Inspecting the SageMaker Autopilot experiment's results and artifactsGetting readyHow to do it…How it works…Performing Automatic Model Tuning with the SageMaker XGBoost built-in algorithmGetting readyHow to do it…How it works…There's more…See alsoAnalyzing the Automatic Model Tuning job resultsGetting readyHow to do it…How it works…
Chapter 7: Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor
Technical requirementsGenerating a synthetic dataset and using SageMaker Feature Store for storage and managementGetting readyHow to do it…How it works…Querying data from the offline store of SageMaker Feature Store and uploading it to Amazon S3Getting readyHow to do it…How it works…There's more…Detecting pre-training bias with SageMaker ClarifyGetting readyHow to do it…How it works…There's more…Detecting post-training bias with SageMaker ClarifyGetting readyHow to do it…How it works…Enabling ML explainability with SageMaker ClarifyGetting readyHow to do it…How it works…Deploying an endpoint from a model and enabling data capture with SageMaker Model MonitorGetting readyHow to do it…How it works…Baselining and scheduled monitoring with SageMaker Model MonitorGetting readyHow 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 requirementsGenerating a synthetic dataset for text classification problemsGetting readyHow to do it…How it works…There's more…Preparing the test dataset for batch transform inference jobsGetting readyHow to do it…How it works…Training and deploying a BlazingText modelGetting readyHow to do it…How it works…See moreUsing Batch Transform for inferenceGetting readyHow to do it…How it works…See alsoPreparing the datasets for image classification using the Apache MXNet Vision Datasets classesGetting readyHow to do it…How it works…See alsoTraining and deploying an image classifier using the built-in Image Classification Algorithm in SageMakerGetting readyHow to do it…How it works…There's more…See alsoGenerating a synthetic time series datasetGetting readyHow to do it…How it works…Performing the train-test split on a time series datasetGetting readyHow to do it…How it works…Training and deploying a DeepAR modelGetting readyHow to do it…How it works…Performing probabilistic forecasting with a deployed DeepAR modelGetting readyHow to do it...How it works…See also

Chapter 9: Managing Machine Learning Workflows and Deployments
Technical requirementsWorking with Hugging Face modelsGetting readyHow to do it…How it works…There's more…See alsoPreparing the prerequisites of a multi-model endpoint deploymentGetting readyHow to do it…How it works…Hosting multiple models with multi-model endpointsGetting readyHow to do it…How it works…Setting up A/B testing on multiple models with production variantsGetting readyHow to do it…How it works…There's more…Preparing the Step Functions execution roleGetting readyHow to do it…How it works…Managing ML workflows with AWS Step Functions and the Data Science SDKGetting readyHow to do it…How it works…See alsoManaging ML workflows with SageMaker PipelinesGetting readyHow to do it…How it works…See also
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Overview

Machine Learning with Amazon SageMaker Cookbook is your hands-on guide to mastering ML experiments and workflows on AWS. With 80 practical recipes, you'll learn to build, train, and deploy ML models using TensorFlow, PyTorch, and Amazon SageMaker's advanced tools. By the end, you'll confidently apply these skills to real-world projects for impactful results.

What this Book will help me do

Train and deploy ML models for tasks like NLP and time-series forecasting using Amazon SageMaker.
Work with deep learning frameworks such as TensorFlow, PyTorch, and Hugging Face.
Automate ML workflows and deployments using SageMaker capabilities.
Ensure your models are transparent and unbiased with SageMaker Clarify and Model Monitor.
Customize solutions for ML on AWS with custom algorithm containers.

Author(s)

Joshua Arvin Lat is a seasoned data scientist and ML practitioner with extensive experience in real-world machine learning deployments. He specializes in AWS-based machine learning and deeply understands Amazon SageMaker's vast potential. With this book, Joshua offers a hands-on approach that empowers learners to tackle and innovate in the ML domain.

Who is it for?

This book is tailored for data scientists, developers, and ML practitioners eager to harness Amazon SageMaker for their projects. It's ideal for those looking to elevate their skills with hands-on recipes. Readers should possess foundational knowledge in machine learning, AWS, and Python for the best experience.

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