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

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octavian Jul 08, 2023

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

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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.

Chandra May 23, 2022

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.

Devarshi May 21, 2022

Received the book today ! Its a piece of art !!! Really impressed by the content.

Amazon Customer Mar 31, 2022

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!

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

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

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