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The Machine Learning Solutions Architect Handbook
The Machine Learning Solutions Architect Handbook

The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI , Second Edition

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The Machine Learning Solutions Architect Handbook

ML challenges

Over the years, I have worked on many real-world problems using ML solutions and encountered different challenges faced by the different industries during ML adoptions.I often get the same question when working on ML projects: We have a lot of data – can you help us figure out what insights we can generate using ML? I refer to companies with this question as having the business use case challenge. Not being able to identify business use cases for ML is a very big hurdle for many companies. Without a properly identified business problem and its value proposition and benefit, it becomes difficult to initiate an ML project.In my conversations with different companies across their industries, data-related challenges emerge as frequent issue. This includes data quality, data inventory, data accessibility, data governance, and data availability. This problem affects both data-poor and data-rich companies and is often exacerbated by data silos, data security, and industry...

ML solutions architecture

When I initially worked with companies as an ML solutions architect, the landscape was quite different from what it is now. The focus was mainly on data science and modeling, and the problems at hand were small in scope. Back then, most of the problems could be solved using simple ML techniques. The datasets were also small, and the infrastructure required was also not demanding. The scope of the ML initiative at these companies was limited to a few data scientists or teams. As an ML architect at that time, I primarily needed to have solid data science skills and general cloud architecture knowledge to get the job done.In the more recent years, the landscape of ML initiatives has become more intricate and multifaceted, necessitating involvement from a broader range of functions and personas at companies. My engagement has expanded to include discussions with business executives about ML strategies and organizational design to faciliate the broad adoption of AI...

Testing your knowledge

Great job! You have reached to the end of the chapter. Now, let's put your newly acquired knowledge to the test and see if you've understood and retained the information presented.Take a look at the list of the following scenarios and determine which of the three ML types can be applied (supervised, unsupervised, or reinforcement):

  1. There is a list of online feedback on products. Each comment has been labeled with a sentiment class (for example, positive, negative, neutral). You have been asked to build an ML model to predict the sentiment of new feedback.
  2. You have historical house pricing information and details about the house, such as zip code, number of bedrooms, house size, and house condition. You have been asked to build an ML model to predict the price of a house.
  3. You have been asked to identify potentially fraudulent transactions on your company's e-commerce site. You have data such as historical transaction data, user information, credit...

Summary

You now have a solid understanding of various concepts such as AI, ML, and the essential steps of the end-to-end ML life cycle. Additionally, you have gained insight into the core functions of ML solutions architecture and how it plays a crucial role in the success of an ML project. With your newfound knowledge, you can differentiate between different types of ML and identify their application in solving business problems. Moreover, you have learned that it is crucial to have a deep understanding of business and data to achieve success in an ML project, besides modeling and engineering. Lastly, you have gained an understanding of the significance of ML solutions architecture and how it fits into the ML life cycle.In the upcoming chapter, we will dive into various ML use cases across different industries, such as financial services and media and entertainment, to gain further insights into the practical applications of ML.

ML use cases in manufacturing

The manufacturing industry is a vast sector that is responsible for creating a wide range of physical products, such as consumer goods, electronics, automobiles, furniture, building materials, and more. Each sub-sector of manufacturing requires a specific set of tools, resources, and expertise to successfully produce the desired products.

The manufacturing process generally involves several stages, including product design, prototyping, production, and post-manufacturing service and support. During the design phase, manufacturers work on conceptualizing and planning the product. This includes defining the product’s features, materials, and production requirements. In the prototyping stage, a small number of products are created to test their functionality and performance.

Once the product design has been finalized, manufacturing and assembling takes place. This is the stage where raw materials are transformed into finished products. Quality...

ML use cases in retail

The retail industry is a sector that sells consumer products directly to customers, either through physical retail stores or online platforms. Retailers acquire their merchandise from wholesale distributors or manufacturers directly. Over the years, the retail industry has undergone significant changes. The growth of e-commerce has outpaced that of traditional retail businesses, compelling brick-and-mortar stores to adapt and innovate in-store shopping experiences to remain competitive. Retailers are exploring new approaches to enhance the shopping experience across both online and physical channels. Recent developments such as social commerce, augmented reality, virtual assistant shopping, smart stores, and 1:1 personalization have become key differentiators in the retail industry.

The retail industry is currently undergoing a transformation fueled by AI and ML technologies. Retailers are utilizing these technologies to optimize inventory, predict consumer...

ML use cases in the automotive industry

The automotive industry has undergone significant transformation in recent years, with technology playing a key role in shaping its evolution. AI and ML have emerged as powerful tools for automakers and suppliers to improve efficiency, safety, and customer experience. From production lines to connected cars, AI and ML are being used to automate processes, optimize operations, and enable new services and features.

Autonomous vehicles

One of the most significant applications of AI and ML in the automotive industry is in autonomous driving. Automakers and tech companies are leveraging these technologies to build self-driving vehicles that can safely navigate roads and highways without human intervention. AI and ML algorithms are used to process data from sensors, cameras, and other inputs to make real-time decisions and actions, such as braking or changing lanes.

The system architecture of an autonomous vehicle (AV) consists of 3 main...

Summary

Throughout this chapter, we have explored various industries and the ways in which they are utilizing ML to solve business challenges and drive growth. From finance and healthcare to retail and automotive, we have seen how ML can improve processes, generate insights, and enhance the customer experience. The examples within this chapter have hopefully sparked ideas you can now bring to stakeholders to kickstart an ML roadmap discussion and think creatively about the potential high-impact applications in your own organizations.

As we move into the next chapter, we will delve deeper into the mechanics of ML, exploring the fundamental concepts behind how machines learn and some of the most widely used algorithms in the field. This will provide you with a solid foundation for understanding how ML is applied in practice to solve various ML problems.

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Key benefits

  • Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling
  • Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions
  • Understand the generative AI lifecycle, its core technologies, and implementation risks

Description

David Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills. You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI. By the end of this book , you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.

Who is this book for?

This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.

What you will learn

  • Apply ML methodologies to solve business problems across industries
  • Design a practical enterprise ML platform architecture
  • Gain an understanding of AI risk management frameworks and techniques
  • 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 artificial intelligence services and custom models
  • Dive into generative AI with use cases, architecture patterns, and RAG

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Publication date : Apr 15, 2024
Length: 602 pages
Edition : 2nd
Language : English
ISBN-13 : 9781805124825
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Publication date : Apr 15, 2024
Length: 602 pages
Edition : 2nd
Language : English
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Table of Contents

18 Chapters
Navigating the ML Lifecycle with ML Solutions Architecture Chevron down icon Chevron up icon
Exploring ML Business Use Cases Chevron down icon Chevron up icon
Exploring ML Algorithms Chevron down icon Chevron up icon
Data Management for ML Chevron down icon Chevron up icon
Exploring Open-Source ML Libraries Chevron down icon Chevron up icon
Kubernetes Container Orchestration Infrastructure Management Chevron down icon Chevron up icon
Open-Source ML Platforms Chevron down icon Chevron up icon
Building a Data Science Environment Using AWS ML Services Chevron down icon Chevron up icon
Designing an Enterprise ML Architecture with AWS ML Services Chevron down icon Chevron up icon
Advanced ML Engineering Chevron down icon Chevron up icon
Building ML Solutions with AWS AI Services Chevron down icon Chevron up icon
AI Risk Management Chevron down icon Chevron up icon
Bias, Explainability, Privacy, and Adversarial Attacks Chevron down icon Chevron up icon
Charting the Course of Your ML Journey Chevron down icon Chevron up icon
Navigating the Generative AI Project Lifecycle Chevron down icon Chevron up icon
Designing Generative AI Platforms and Solutions Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
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5 star 80%
4 star 10%
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2 star 5%
1 star 5%
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Rohith Oct 25, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Subscriber review Packt
Chelsy Apr 17, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Thanks David!
Amazon Verified review Amazon
Kitty May 22, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Amazon Verified review Amazon
Felipe Lopez Apr 15, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Dr John Jones Jun 13, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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