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Key benefits

Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud

Build an efficient data science environment for data exploration, model building, and model training

Learn how to implement bias detection, privacy, and explainability in ML model development

Description

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.

Who is this book for?

This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.

What you will learn

Apply ML methodologies to solve business problems

Design a practical enterprise ML platform architecture

Implement MLOps for ML workflow automation

Build an end-to-end data management architecture using AWS

Train large-scale ML models and optimize model inference latency

Create a business application using an AI service and a custom ML model

Use AWS services to detect data and model bias and explain models

What do you get with Audiobook?

Download a zip folder containing audio files (MP3) and a supplementary PDF

Access this title in our online player

DRM FREE - Listen whenever, wherever and however you want

Frequently bought together

The Machine Learning Solutions Architect Handbook

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Machine Learning Engineering with Python

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Can$69.99

Total Can$ 253.97

Josh B. Mar 01, 2022

I just read the book “The Machine Learning Solutions Architect Handbook” by my colleague at Amazon Web Services, David Ping. I was honored that Packt reached out to me to ask if I would post my thoughts about the book and provided me with an advanced copy of the book.Onto the book itself. This book successfully takes on a complex challenge of presenting both the fundamentals of machine learning, while also diving deep into concrete discussions and architectures of how to actually execute ML workloads at production scale.What I liked most about this book is that while the author gives an excellent overview of how to deploy AI/ML workloads on AWS* specifically, he also spends many pages explaining the fundamentals of machine learning in an organized, clear manner in the context of business enterprise workloads. This book addresses real-life questions like: * What types of business problems and use cases are amenable to AI/ML? * How, on a basic level, does AI/ML work? What are common algorithms used nowadays? * What are the frequently used AI/ML libraries, and how do they work? * What are the most commonly used solutions for AI/ML orchestration?This book does a good job of answering questions like this and many others. Reading this book will give you a strong sense of how to architect AI/ML workloads for scale, reproducibility and with proper governance and orchestration. In addition, the code within the repository associated with the book gives readers a good place to start in a hands-on fashion.This book especially fills an important niche for data scientists and machine learning engineers who want to expand their horizons into the infrastructure of doing AI/ML at scale. I highly recommend this book for anyone who wants to understand what designing and productionizing AI/ML workloads is all about.*See Chapter 8 specifically for a great example of deploying ML workloads using Amazon SageMaker.

Amazon Verified review

CTO in a big bank Jul 31, 2022

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.

Amazon Verified review

octavian Jul 08, 2023

This is a great book that summarises most important aspects of Ml in production. Enjoyed reading it.

Amazon Verified review

JL Feb 25, 2022

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!

Amazon Verified review

Vik Mar 27, 2022

As stated in its title, this is a fantastic ML handbook for Solution architects.The author does a great job introducing concepts of ML and provides great context and motivation for application of ML solution for various business use case across multiple domains.What I particularly liked about the book is how it covers all key practical aspects of implementing a end to end ML solution, be it Data management /Kubernetes infra management/Open source ML libraries. The book provides a comprehensive introduction to various topics/concepts in implementation of a ML solution including aspects of Enterprise level implementation concerns.While the book does a good job in covering various topics related ML, it does not dig deep into any particular area (which to be fair would be out of scope of this book). In terms of cloud solution , the author focuses primarily on AWS, it would have been nice to see some example for other Cloud solution providers.Overall as I would say its a well rounded book, I would highly recommend this book for any solution architect trying to get a handle on ML, it provides a great start! And is very approachable and easy to follow along!

Amazon Verified review

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

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