Cloud Native AI and Machine Learning on AWS: Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services (English Edition)
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Bring elasticity and innovation to Machine Learning and AI operations
Key Features
● Coverage includes a wide range of AWS AI and ML services to help you speedily get fully operational with ML.
● Packed with real-world examples, practical guides, and expert data science methods for improving AI/ML education on AWS.
● Includes ready-made, purpose-built models as AI services and proven methods to adopt MLOps techniques.
Description
Using machine learning and artificial intelligence (AI) in existing business processes has been successful. Even AWS’s ML and AI services make it simple and economical to conduct machine learning experiments. This book will show readers how to use the complete set of AI and ML services available on AWS to streamline the management of their whole AI operation and speed up their innovation.
In this book, you’ll learn how to build data lakes, build and train machine learning models, automate MLOps, ensure maximum data reusability and reproducibility, and much more. The applications presented in the book show how to make the most of several different AWS offerings, including Amazon Comprehend, Amazon Rekognition, Amazon Lookout, and AutoML. This book teaches you to manage massive data lakes, train artificial intelligence models, release these applications into production, and track their progress in real-time. You will learn how to use the pre-trained models for various tasks, including picture recognition, automated data extraction, image/video detection, and anomaly detection.
Every step of your Machine Learning and AI project’s development process is optimised throughout the book by utilising Amazon’s pre-made, purpose-built AI services.
What you will learn
● Learn how to build, deploy, and manage large-scale AI and ML applications on AWS.
● Get your hands dirty with AWS AI services like SageMaker, Comprehend, Rekognition, Lookout, and AutoML.
● Master data transformation, feature engineering, and model training with Amazon SageMaker modules.
● Use neural networks, distributed learning, and deep learning algorithms to improve ML models.
● Use AutoML, SageMaker Canvas, and Autopilot for Model Deployment and Evaluation.
● Acquire expertise with Amazon SageMaker Studio, Jupyter Server, and ML frameworks such as TensorFlow and MXNet.
Who this book is for
Data Engineers, Data Scientists, AWS and Cloud Professionals who are comfortable with machine learning and the fundamentals of Python will find this book powerful. Familiarity with AWS would be helpful but is not required.
Table of Contents
1. Introducing the ML Workflow
2. Hydrating the Data Lake
3. Predicting the Future With Features
4. Orchestrating the Data Continuum
5. Casting a Deeper Net (Algorithms and Neural Networks)
6. Iteration Makes Intelligence (Model Training and Tuning)
7. Let George Take Over (AutoML in Action)
8. Blue or Green (Model Deployment Strategies)
9. Wisdom at Scale with Elastic Inference
10. Adding Intelligence with Sensory Cognition
11. AI for Industrial Automation
12. Operationalized Model Assembly (MLOps and Best Practices)
Publisher : BPB Publications (February 14, 2023)
Language : English
Paperback : 400 pages
ISBN-10 : 9355513267
ISBN-13 : 978-9355513267
Item Weight : 10.6 ounces
Dimensions : 7.5 x 0.91 x 9.25 inches
Customers say
Customers find the content relevant and interesting. They appreciate the simple, easy-to-understand style and the great overview it provides to anyone getting started with AWS cloud AI/ML. Readers also mention the book comes with easy implementation code.
AI-generated from the text of customer reviews
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9 reviews for Cloud Native AI and Machine Learning on AWS: Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services (English Edition)
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Suprakash Dutta –
Excellent book to get started or expand skill set on cloud native AI/ML services and strategies
I’ve been reading this book for almost a week now and been very impressed by the way the narration is done. This book is pragmatic, comprehensive and explains complicated concepts with ease. The step by step descriptions, architectures, flow-charts and code examples are all industry focused making it very relevant for anyone who wants to learn AWS AI/ML strategies fast to implement in their industries. While this book is written by focusing on AWS services, however, these skills can be pretty much used across the board with other hyper-scalers/cloud. Thoroughly impressed!
pratyush ranjan –
A simplified book to understand AWS Machine learning technology
This book provides a great overview to anyone getting started with AWS cloud AI/ML technology. I love the interesting and easy way to explain concepts. This book comes with easy implementation code in case you want to implement a use case such as sentiment analysis. I love the fun elements introduced by author to explain difficult concepts.
Tube2IC –
Good topic coverage and complex concepts simplified
Great book that covered a lot of ground. Authors explain ed complex concepts in a simple easy to understand style. Good hands on examples as well.
Ash –
Good coverage of AWS AI/ML services with code examples
A good resource to learn how to implement the end-to-end ML lifecycle from sourcing data to deployment of a trained model on Amazon SageMaker. The authors have covered some basic foundational concepts of machine learning in good detail, but for others, readers will have to follow the provided web links to get a good understanding. One can easily run the accompanied code examples in the AWS cloud by following the instructions provided in the text for opening an AWS account and setting up the SageMaker Studio environment.
Apratim Shaw –
Finally, the book I have been waiting for!
It is easy to find material for AI/ML fundamentals. Also, there are lots of books on AI/ML programming. However, if youâre like me, youâre probably looking for a book that goes beyond the basics and can help you leverage cloud native AI/ML capabilities to apply to your real world opportunities. This book is exactly what I need.
ARUNPRASATH SHANKAR –
A Comprehensive Journey into the World of ML Workflows with AWS SageMaker
“Cloud Native AI and Machine Learning on AWS” is an outstanding resource for individuals seeking to comprehend and implement machine learning workflows using AWS. The book covers every phase of the ML lifecycle, including data management, feature engineering, model training, and operationalizing ML models.The authors provide a comprehensive understanding of how to use AWS infrastructure for ML workflows, covering various AWS AI and ML services, deployment and hosting options, and ML-Ops. This well-written book is clearly organized into a progression of topics that build on top of one another. It is suitable not only for beginners and intermediate-level ML practitioners but also serves as a reference guide for experienced professionals. I highly recommend this book to anyone interested in learning about the latest trends and best practices in machine learning workflows.
Sreyoshi –
Informative and engaging
This book is a wonderful resource to get started with AI and ML with AWS – straight from the horseâs mouth, from authors who are experts in leveraging these tools professionally. Itâs a well written, interesting, and informative textbook that is sure to help IT professionals and students. Engaging chapters will enhance your learning experience, and code snippets will allow you to get started with your new project! Strongly recommend!
Kimberly Martin-McClure –
Great introduction to the many features of SageMaker
I highly recommend this book to anyone that is new to the many features of SageMaker. The authors present this complex topic in clear and entertaining way with many examples included.
amit kumar –
Most of the pages are not readable, the printing quality is very poor