Preface
Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker.
You'll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you'll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You'll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you'll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy.
By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring, to scaling, deployment, and automation.
Who this book is for
This book is for software engineers, ML developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality ML models without worrying about infrastructure. Knowledge of the basics of AWS is required to grasp the concepts covered in this book more effectively. Some understanding of ML concepts and the Python programming language will also be beneficial.
What this book covers
Chapter 1, Getting Started with Amazon SageMaker, provides an overview of Amazon SageMaker, what its capabilities are, and how it helps solve many pain points faced by ML projects today.
Chapter 2, Handling Data Preparation Techniques, discusses data preparation options. Although this it isn't the core subject of the book, data preparation is a key topic in ML, and it should be covered at a high level.
Chapter 3, AutoML with Amazon SageMaker AutoPilot, shows you how to build, train, and optimize ML models automatically with Amazon SageMaker AutoPilot.
Chapter 4, Training Machine Learning Models, shows you how to build and train models using the collection of statistical ML algorithms built into Amazon SageMaker.
Chapter 5, Training Computer Vision Models, shows you how to build and train models using the collection of computer vision algorithms built into Amazon SageMaker.
Chapter 6, Training Natural Language Processing Models, shows you how to build and train models using the collection of natural language processing algorithms built into Amazon SageMaker.
Chapter 7, Extending Machine Learning Services Using Built-In Frameworks, shows you how to build and train ML models using the collection of built-in open source frameworks in Amazon SageMaker.
Chapter 8, Using Your Algorithms and Code, shows you how to build and train ML models using your own code on Amazon SageMaker, for example, R or custom Python.
Chapter 9, Scaling Your Training Jobs, shows you how to distribute training jobs to many managed instances, using either built-in algorithms or built-in frameworks.
Chapter 10, Advanced Training Techniques, shows you how to leverage advanced training in Amazon SageMaker.
Chapter 11, Deploying Machine Learning Models, shows you how to deploy ML models in a variety of configurations.
Chapter 12, Automating Deployment Tasks, shows you how to automate the deployment of ML models on Amazon SageMaker.
Chapter 13, Optimizing Cost and Performance, shows you how to optimize model deployments, both from an infrastructure perspective and from a cost perspective.
To get the most out of this book
You will need a functional AWS account to run everything.
If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
Download the example code files
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
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- Enter the name of the book in the Search box and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Learn-Amazon-SageMaker. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781800208919_Colorimages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "You can use the describe-spot-price-history API to collect this information programmatically."
A block of code is set as follows:
od = sagemaker.estimator.Estimator( container, role, train_instance_count=2, train_instance_type='ml.p3.2xlarge', train_use_spot_instances=True, train_max_run=3600, # 1 hours train_max_wait=7200, # 2 hour output_path=s3_output)
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
[<sagemaker.model_monitor.model_monitoring.MonitoringExecution at 0x7fdd1d55a6d8>,<sagemaker.model_monitor.model_monitoring.MonitoringExecution at 0x7fdd1d581630>,<sagemaker.model_monitor.model_monitoring.MonitoringExecution at 0x7fdce4b1c860>]
Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "We can find more information about our monitoring job in the SageMaker console, in the Processing jobs section."
Tips or important notes
Appear like this.
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