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Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for developers and data scientists
Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor
Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerAnalyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniquesImprove productivity by training and fine-tuning machine learning models in productionBook DescriptionAmazon 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.
What you will learnCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Become well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and NLP models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is forThis book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.
Table of ContentsGetting Started with Amazon SageMaker Handling Data Preparation TechniquesAutoML with Amazon SageMaker AutoPilotTraining Machine Learning ModelsTraining Computer Vision Models Training Natural Language Processing Models Extending Machine Learning Services Using Built-In FrameworksUsing Your Algorithms and CodeScaling Your Training JobsAdva
Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerAnalyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniquesImprove productivity by training and fine-tuning machine learning models in productionBook DescriptionAmazon 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.
What you will learnCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Become well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and NLP models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is forThis book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.
Table of ContentsGetting Started with Amazon SageMaker Handling Data Preparation TechniquesAutoML with Amazon SageMaker AutoPilotTraining Machine Learning ModelsTraining Computer Vision Models Training Natural Language Processing Models Extending Machine Learning Services Using Built-In FrameworksUsing Your Algorithms and CodeScaling Your Training JobsAdva
- GenresTechnology
490 pages, Kindle Edition
Published August 27, 2020
21 people are currently reading
39 people want to read
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Displaying 1 - 4 of 4 reviews
June 7, 2021
I've read it just after "Data Science on AWS" (published by O'Reilly) and it's definitely the better of those two books. This definitely is some sort of surprise, because O'Reilly is quite known due to the high bar of quality while PacktPub, well ... not really :)
Anyway, what did I like and not like about this book?
1. It's actually possible to learn stuff based thanks to this book. Scenarios are reasonable, well-paced, quite well explained and simple enough. They are STILL focused on AWS services (not classes of the problems to be solved), but the final effect is far better than the chaotic cocktail from "Data Science ..."
2. Examples use a lot of interesting public data sets. By 'interesting' I mean really interesting - you really want to play with this stuff.
3. There are very few distractors - topics that do not really belong and just artificially increase the size of the book.
4. The chapters with practitioners' advice are VERY good. No generic bullshit - this stuff is really helpful. I mean chapters: 9, 10, 13.
5. The structure of the book supports splitting it up, so e.g. individual chapters can be published as long blog post series or booklets. That's a great idea, but sometimes a bit annoying (e.g. because of some repetitions).
6. As always, PacktPub sucks incredibly when it comes to formatting. I was reading this book via Safari Online and the code samples were VERY hard to read - that itself should decrease the book's rating my 1 star. It's unacceptable in 2021.
7. I praise this book, but it's still far from how I imagine a good intro book on managed AIML services on AWS. As I've mentioned, it's driven by the structure and organization of AWS services, instead of on categories and differences between real-life problems. Additionally, I miss some conceptual overview on how to design AIML services in general (using managed services). The readers know that the code samples in the book are tutorialesque and may have an actual problem of how to start "for real".
Anyway, the book is really OK. If you're interested in AIML and want a quickstart on managed services within that area - it's a reasonable choice.
Anyway, what did I like and not like about this book?
1. It's actually possible to learn stuff based thanks to this book. Scenarios are reasonable, well-paced, quite well explained and simple enough. They are STILL focused on AWS services (not classes of the problems to be solved), but the final effect is far better than the chaotic cocktail from "Data Science ..."
2. Examples use a lot of interesting public data sets. By 'interesting' I mean really interesting - you really want to play with this stuff.
3. There are very few distractors - topics that do not really belong and just artificially increase the size of the book.
4. The chapters with practitioners' advice are VERY good. No generic bullshit - this stuff is really helpful. I mean chapters: 9, 10, 13.
5. The structure of the book supports splitting it up, so e.g. individual chapters can be published as long blog post series or booklets. That's a great idea, but sometimes a bit annoying (e.g. because of some repetitions).
6. As always, PacktPub sucks incredibly when it comes to formatting. I was reading this book via Safari Online and the code samples were VERY hard to read - that itself should decrease the book's rating my 1 star. It's unacceptable in 2021.
7. I praise this book, but it's still far from how I imagine a good intro book on managed AIML services on AWS. As I've mentioned, it's driven by the structure and organization of AWS services, instead of on categories and differences between real-life problems. Additionally, I miss some conceptual overview on how to design AIML services in general (using managed services). The readers know that the code samples in the book are tutorialesque and may have an actual problem of how to start "for real".
Anyway, the book is really OK. If you're interested in AIML and want a quickstart on managed services within that area - it's a reasonable choice.
March 30, 2022
AWS has too many services and is a jungle that needs to be navigated carefully. Else, it is very easy to get lost. This book sticks to Sagemaker alone, while referring to a few services occasionally - such as Lambda, Cloudwatch etc. And it does a good job in doing so. The code snippets proved to be very useful. Being a complex topic, it may require multiple re-reads. Also the index needs improvement. You want to find about some topic and the index doesn't point to it. I thought the book could also focus a bit on talking to sagemaker from an external environment - such as by using boto3.
But Sagemaker has too many capabilities that it might be impossible to cover all of them in one book.
Overall, this is a great book if you are starting with Sagemaker and want to get a single source to get you started.
But Sagemaker has too many capabilities that it might be impossible to cover all of them in one book.
Overall, this is a great book if you are starting with Sagemaker and want to get a single source to get you started.
February 9, 2021
This book was absolutely amazing. It answered and covered almost all of the questions and problems that my colleagues and I, were struggling with. A must read for anyone who's new to SageMaker, or wants to bring their efficiency to the next level.
August 27, 2021
The book offers entry-level knowledge for using SageMaker with various ML tasks as well as how to leverage SageMaker for AutoML and processing. It is not sufficient to give me the full knowledge for working with complex ML model that needs to be ported to SageMaker Script Mode though. I found https://www.coursera.org/learn/ml-pip... to have the missing piece of information for more advanced topics
Displaying 1 - 4 of 4 reviews