Book description
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.
Table of contents
- Machine Learning with Amazon SageMaker Cookbook
- Contributors
- About the author
- About the reviewers
- Preface
-
Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker
- Technical requirements
- Launching an Amazon SageMaker Notebook Instance
- Checking the versions of the SageMaker Python SDK and the AWS CLI
- Preparing the Amazon S3 bucket and the training dataset for the linear regression experiment
- Visualizing and understanding your data in Python
- Training your first model in Python
- Loading a linear learner model with Apache MXNet in Python
- Evaluating the model in Python
- Deploying your first model in Python
- Invoking an Amazon SageMaker model endpoint with the SageMakerRuntime client from boto3
-
Chapter 2: Building and Using Your Own Algorithm Container Image
- Technical requirements
- Launching and preparing the Cloud9 environment
- Setting up the Python and R experimentation environments
- Preparing and testing the train script in Python
- Preparing and testing the serve script in Python
- Building and testing the custom Python algorithm container image
- Pushing the custom Python algorithm container image to an Amazon ECR repository
- Using the custom Python algorithm container image for training and inference with Amazon SageMaker Local Mode
- Preparing and testing the train script in R
- Preparing and testing the serve script in R
- Building and testing the custom R algorithm container image
- Pushing the custom R algorithm container image to an Amazon ECR repository
- Using the custom R algorithm container image for training and inference with Amazon SageMaker Local Mode
-
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
- Generating a synthetic dataset for deep learning experiments
- Preparing the entrypoint TensorFlow and Keras training script
- Training and deploying a TensorFlow and Keras model with the SageMaker Python SDK
- Preparing the entrypoint PyTorch training script
- Preparing the entrypoint PyTorch inference script
- Training and deploying a PyTorch model with the SageMaker Python SDK
- Preparing the entrypoint scikit-learn training script
- Training and deploying a scikit-learn model with the SageMaker Python SDK
- Debugging disk space issues when using local mode
- Debugging container execution issues when using local mode
-
Chapter 4: Preparing, Processing, and Analyzing the Data
- Technical requirements
- Generating a synthetic dataset for anomaly detection experiments
- Training and deploying an RCF model
- Invoking machine learning models with Amazon Athena using SQL queries
- Analyzing data with Amazon Athena in Python
- Generating a synthetic dataset for analysis and transformation
- Performing dimensionality reduction with the built-in PCA algorithm
- Performing cluster analysis with the built-in KMeans algorithm
- Converting CSV data into protobuf recordIO format
- Training a KNN model using the protobuf recordIO training input type
- Preparing the SageMaker Processing prerequisites using the AWS CLI
- Managed data processing with SageMaker Processing in Python
- Managed data processing with SageMaker Processing in R
-
Chapter 5: Effectively Managing Machine Learning Experiments
- Technical requirements
- Synthetic data generation for classification problems
- Identifying issues with SageMaker Debugger
- Inspecting SageMaker Debugger logs and results
- Running and managing multiple experiments with SageMaker Experiments
- Experiment analytics with SageMaker Experiments
- Inspecting experiments, trials, and trial components with SageMaker Experiments
-
Chapter 6: Automated Machine Learning in Amazon SageMaker
- Technical requirements
- Onboarding to SageMaker Studio
- Generating a synthetic dataset with additional columns containing random values
- Creating and monitoring a SageMaker Autopilot experiment in SageMaker Studio (console)
- Creating and monitoring a SageMaker Autopilot experiment using the SageMaker Python SDK
- Inspecting the SageMaker Autopilot experiment's results and artifacts
- Performing Automatic Model Tuning with the SageMaker XGBoost built-in algorithm
- Analyzing the Automatic Model Tuning job results
-
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
- Querying data from the offline store of SageMaker Feature Store and uploading it to Amazon S3
- Detecting pre-training bias with SageMaker Clarify
- Detecting post-training bias with SageMaker Clarify
- Enabling ML explainability with SageMaker Clarify
- Deploying an endpoint from a model and enabling data capture with SageMaker Model Monitor
- Baselining and scheduled monitoring with SageMaker Model Monitor
-
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
- Preparing the test dataset for batch transform inference jobs
- Training and deploying a BlazingText model
- Using Batch Transform for inference
- Preparing the datasets for image classification using the Apache MXNet Vision Datasets classes
- Training and deploying an image classifier using the built-in Image Classification Algorithm in SageMaker
- Generating a synthetic time series dataset
- Performing the train-test split on a time series dataset
- Training and deploying a DeepAR model
- Performing probabilistic forecasting with a deployed DeepAR model
-
Chapter 9: Managing Machine Learning Workflows and Deployments
- Technical requirements
- Working with Hugging Face models
- Preparing the prerequisites of a multi-model endpoint deployment
- Hosting multiple models with multi-model endpoints
- Setting up A/B testing on multiple models with production variants
- Preparing the Step Functions execution role
- Managing ML workflows with AWS Step Functions and the Data Science SDK
- Managing ML workflows with SageMaker Pipelines
- Why subscribe?
- Other Books You May Enjoy
Product information
- Title: Machine Learning with Amazon SageMaker Cookbook
- Author(s):
- Release date: October 2021
- Publisher(s): Packt Publishing
- ISBN: 9781800567030
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