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

David Ping

$8.98 ~~$43.99~~

4.6 (21 Ratings)

Audiobook Jan 2022 8hrs 32mins 1st Edition

David Ping

$8.98 ~~$43.99~~

4.6 (21 Ratings)

Audiobook Jan 2022 8hrs 32mins 1st Edition

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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

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Rohith Oct 25, 2023

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Chelsy Apr 17, 2024

Thanks David!

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Kitty May 22, 2024

As a university CISO, the top concerns regarding AI/ML evolved around data security, ethical use, and governance. • Ensuring the security of data used by AI/ML systems is paramount, as these technologies can process vast amounts of sensitive information. The CISO must ensure that data privacy regulations are adhered to and that the AI/ML systems are protected against cyber threats. • The ethical use of AI/ML is a significant concern. This includes issues such as the potential for bias in AI-generated content and the need for transparency in how AI models are trained and deployed. • Governance of AI/ML involves establishing policies and frameworks that dictate how AI/ML technologies are utilized within the university. This includes setting up clear guidelines for responsible AI use, monitoring compliance, and managing the risks associated with these powerful tools.This book clearly outlines and provide guidelines to help addresses the concerns I have as a university CISO

Felipe Lopez Apr 15, 2024

Purchasing 'The Machine Learning Solutions Architect Handbook' was an absolute game-changer for me. This concise yet comprehensive guide brilliantly covers the intricacies of building and deploying ML workflows in the cloud.Whether you're a cloud architect venturing into ML or a data scientist aiming to operationalize workflows, this book is indispensable. It blends theory with practical exercises in a way that caters to both beginners and seasoned professionals. The inclusion of use cases and an overview of common technologies ensures a holistic understanding, while practical exercises facilitate quick learning. As a Machine Learning Solutions Architect, I'll be keeping this handbook close at hand, ready to revisit its invaluable insights time and again.

Dr John Jones Jun 13, 2024

I've just finished reading this book and what a great read and reference book it is. It is packed with essential ideas and information for the machine learning lifecycle. With my AWS background, it felt incredibly familiar yet practical, covering all aspects of the machine learning lifecycle.Given all the GenAI hype, I particularly enjoyed Chapter 15, "Navigating the Generative AI Project Lifecycle"; David touches on the foundations of generative AI and covers details around generative AI platforms, retrieval-augmented generation (RAG) architecture, as well as practical applications across industries.From foundational ML algorithms to advanced tools and architectures, this book caters to readers at various expertise levels in a readable manner. He covers real-life applications and best practices: sections on robust ML infrastructure, optimisation methods, and AWS frameworks like WAF and CAF provide actionable insights for real-world applications.► Ideal AudienceThis book is an excellent addition for machine learning practitioners, solutions architects, data scientists/engineers implementing advanced AI, and tech leaders/decision-makers seeking strategic implications of ML and AI for their organisations.

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

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