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

David Ping

$57.99

5 (25 Ratings)

Audiobook Jan 2022 8hrs 32mins 1st Edition

David Ping

$57.99

5 (25 Ratings)

Audiobook Jan 2022 8hrs 32mins 1st Edition

What do you get with Audiobook?

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

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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

DRM FREE - Listen whenever, wherever and however you want

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Devarshi May 21, 2022

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

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Jane Mar 04, 2022

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.

Q L Jan 21, 2022

I was lucky to read David's book from cover to cover. It is such a great book that covers every core component of machine learning in practice. Our schools teach machine learning with heavy focus on the science/theory part. That leaves many gaps in applying ML in practice--gaps such as in ML engineering, ML infrastructure, ML pipeline automation, ML model scalability and security. David's book bridges these gaps with purposely selected 12 chapters of content that offers both breadth and depth. As an ML practitioner myself, I found this book super helpful. I highly recommend this book to fresh graduates looking for ML related jobs, current ML practitioners looking for a comprehensive book of guidance, and anyone who wants to learn ML in a comprehensive way.

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 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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