The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

I'm about half way through the book, didn't want to wait till I finish to write a review.This book covers all the important aspects of architecting an AWS ML solution. It addresses not only the major popular ML services such as SageMaker, it also looks into the data engineering ETL services offered by AWS such as Glue, AWS Lambda, Athena, and others.As a data engineer myself, this is an excellent book for me to learn and understand how cloud data pipelines are implemented in ML and data science architecture. I also love the hands-on exercises, easy to follow.For those who may be new to the AWS cloud, and feel overwhelmed with the number of various services in the AWS ecosystem, this book will easily save many hours of research and get right to the point. I strongly recommend this book.

Machine learning solutions architect handbook by David Ping is a must read for all levels of data scientist, ML engineers and data science/analytics executives. This book covers the length and breadth of building and operationalizing ML models with open source tools and also AWS AI services. Though this book covers the ML environment in AWS, it covers various facets of ML model building, deployment, maintenance and governance.I feel this book serves various personas in AI/ML domain:1.Data scientists could expand their knowledge on how to train and serve large scale models.2.ML engineers/solution architects could expand their knowledge on Kubernetes & AWS features to build enterprise scale data science/ML platforms.3.Data science/analytics leaders could learn about various AI/ML use cases, AWS AI services and get familiarized with various AWS tools in AI/ML architecture.This book not only provides tutorials for in-depth understanding of the concepts but also is easy to understand if one wants to get a high level overview. Highly recommend it.

What an incredible and practical book! I was a novice at ML before reading this. David starts from the basics in plain English, moves on to actually create on AWS each building block of an ML platform for the enterprise, covering every aspect to make it operational in a business. Bravo. It also forced me to learn Python, which is never a waste of time. Highly recommended for any CIO/CTO/CDO, and everyone in their team.

I bought this book to learn what a ML solutions architect does as I have not seen a similar book like this. It is very clear that the author is an expert practitioner in the space. In the book, the author referenced his own personal experiences in building ML solutions for real-world use cases. And he covers the science, technology, and governance considerations for putting ML into production. Highly recommended!

It is very clear that the author is an expert in the ML space. In the book, the author referenced many real-world use cases, very useful and interesting read. Highly recommended!

I highly recommend this book for people with cloud experience to learn ML. This book introduces to the reader the different data science, ML technology, ML architecture, and business topics in a way that’s easy to understand. The book also covers enough depth and detail in each area to be practical, while providing a good conceptual foundation for people who want to get advanced into different ML topics.

A very good book to guide you on learning AI. I especially like the chapters on ML introduction and business use cases.

I highly recommend this book for people who want to learn about machine learning. This book introduces different machine learning technologies and machine learning architecture. It also includes business topics to show how machine learning can be implemented in the real world. Great Book!