Google Machine Learning and Generative AI for Solutions Architects

Google Machine Learning and Generative AI for Solutions Architects
Author: Kieran Kavanagh
Publsiher: Packt Publishing Ltd
Total Pages: 552
Release: 2024-06-28
Genre: Computers
ISBN: 9781803247021

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Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectively Key Features Understand key concepts, from fundamentals through to complex topics, via a methodical approach Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionNearly all companies nowadays either already use or are trying to incorporate AI/ML into their businesses. While AI/ML research is undoubtedly complex, the building and running of apps that utilize AI/ML effectively is tougher. This book shows you exactly how to design and run AI/ML workloads successfully using years of experience some of the world’s leading tech companies have to offer. You’ll begin by gaining a clear understanding of essential fundamental AI/ML concepts, before moving on to grasp complex topics with the help of examples and hands-on activities. This will help you eventually explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. As you advance, you’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these challenges. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You’ll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process. By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.What you will learn Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark Source, understand, and prepare data for ML workloads Build, train, and deploy ML models on Google Cloud Create an effective MLOps strategy and implement MLOps workloads on Google Cloud Discover common challenges in typical AI/ML projects and get solutions from experts Explore vector databases and their importance in Generative AI applications Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows Who this book is for This book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.

GOOGLE MACHINE LEARNING AND GENERATIVE AI FOR SOLUTIONS ARCHITECTS

GOOGLE MACHINE LEARNING AND GENERATIVE AI FOR SOLUTIONS ARCHITECTS
Author: KIERAN KAVANAGH (O.C.D.)
Publsiher: Unknown
Total Pages: 0
Release: 2024
Genre: Electronic Book
ISBN: 1803245271

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The Machine Learning Solutions Architect Handbook

The Machine Learning Solutions Architect Handbook
Author: David Ping
Publsiher: Packt Publishing Ltd
Total Pages: 442
Release: 2022-01-21
Genre: Computers
ISBN: 9781801070416

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Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features 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 Book DescriptionWhen 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. 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 Who this book is 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.

Google Machine Learning and Generative AI for Solutions Architects

Google Machine Learning and Generative AI for Solutions Architects
Author: Kieran Kavanagh
Publsiher: Packt Publishing Ltd
Total Pages: 552
Release: 2024-06-28
Genre: Computers
ISBN: 9781803247021

Download Google Machine Learning and Generative AI for Solutions Architects Book in PDF, Epub and Kindle

Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectively Key Features Understand key concepts, from fundamentals through to complex topics, via a methodical approach Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionNearly all companies nowadays either already use or are trying to incorporate AI/ML into their businesses. While AI/ML research is undoubtedly complex, the building and running of apps that utilize AI/ML effectively is tougher. This book shows you exactly how to design and run AI/ML workloads successfully using years of experience some of the world’s leading tech companies have to offer. You’ll begin by gaining a clear understanding of essential fundamental AI/ML concepts, before moving on to grasp complex topics with the help of examples and hands-on activities. This will help you eventually explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. As you advance, you’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these challenges. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You’ll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process. By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.What you will learn Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark Source, understand, and prepare data for ML workloads Build, train, and deploy ML models on Google Cloud Create an effective MLOps strategy and implement MLOps workloads on Google Cloud Discover common challenges in typical AI/ML projects and get solutions from experts Explore vector databases and their importance in Generative AI applications Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows Who this book is for This book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.

The Machine Learning Solutions Architect Handbook

The Machine Learning Solutions Architect Handbook
Author: David Ping
Publsiher: Packt Publishing Ltd
Total Pages: 603
Release: 2024-04-15
Genre: Computers
ISBN: 9781805124825

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Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook Key Features 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 Book DescriptionDavid 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.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 Who this book is 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.

Solutions Architect s Handbook

Solutions Architect s Handbook
Author: Saurabh Shrivastava,Neelanjali Srivastav
Publsiher: Packt Publishing Ltd
Total Pages: 579
Release: 2024-03-29
Genre: Computers
ISBN: 9781835084366

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From fundamentals and design patterns to the latest techniques such as generative AI, machine learning and cloud native architecture, gain all you need to be a pro Solutions Architect crafting secure and reliable AWS architecture. Key Features Hits all the key areas -Rajesh Sheth, VP, Elastic Block Store, AWS Offers the knowledge you need to succeed in the evolving landscape of tech architecture - Luis Lopez Soria, Senior Specialist Solutions Architect, Google A valuable resource for enterprise strategists looking to build resilient applications - Cher Simon, Principal Solutions Architect, AWS Book DescriptionMaster the art of solution architecture and excel as a Solutions Architect with the Solutions Architect's Handbook. Authored by seasoned AWS technology leaders Saurabh Shrivastav and Neelanjali Srivastav, this book goes beyond traditional certification guides, offering in-depth insights and advanced techniques to meet the specific needs and challenges of solutions architects today. This edition introduces exciting new features that keep you at the forefront of this evolving field. Large language models, generative AI, and innovations in deep learning are cutting-edge advancements shaping the future of technology. Topics such as cloud-native architecture, data engineering architecture, cloud optimization, mainframe modernization, and building cost-efficient and secure architectures remain important in today's landscape. This book provides coverage of these emerging and key technologies and walks you through solution architecture design from key principles, providing you with the knowledge you need to succeed as a Solutions Architect. It will also level up your soft skills, providing career-accelerating techniques to help you get ahead. Unlock the potential of cutting-edge technologies, gain practical insights from real-world scenarios, and enhance your solution architecture skills with the Solutions Architect's Handbook.What you will learn Explore various roles of a solutions architect in the enterprise Apply design principles for high-performance, cost-effective solutions Choose the best strategies to secure your architectures and boost availability Develop a DevOps and CloudOps mindset for collaboration, operational efficiency, and streamlined production Apply machine learning, data engineering, LLMs, and generative AI for improved security and performance Modernize legacy systems into cloud-native architectures with proven real-world strategies Master key solutions architect soft skills Who this book is for This book is for software developers, system engineers, DevOps engineers, architects, and team leaders who already work in the IT industry and aspire to become solutions architect professionals. Solutions architects who want to expand their skillset or get a better understanding of new technologies will also learn valuable new skills. To get started, you'll need a good understanding of the real-world software development process and some awareness of cloud technology.

Generative AI on AWS

Generative AI on AWS
Author: Chris Fregly,Antje Barth,Shelbee Eigenbrode
Publsiher: "O'Reilly Media, Inc."
Total Pages: 323
Release: 2023-11-13
Genre: Electronic Book
ISBN: 9781098159184

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Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock

The Machine Learning Solutions Architect Handbook

The Machine Learning Solutions Architect Handbook
Author: David Ping
Publsiher: Unknown
Total Pages: 0
Release: 2023
Genre: Electronic Book
ISBN: 1805122509

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Improve your product knowledge and ownership while building secure and scalable machine learning platformsPurchase of the print or Kindle book includes a free PDF eBook.Key FeaturesSolve large-scale machine learning challenges in the cloud with a variety of open-source and AWS tools and frameworksApply risk management techniques in the machine learning lifecycleUnderstand the key challenges and risks around implementing generative AI and learn architecture patterns for some solutionsBook DescriptionDavid Ping, Head of ML Solutions Architecture at AWS, provides valuable insights and practical examples for becoming a highly skilled ML solutions architect, linking technical architecture to business-related skills.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 focus on carefully selected and updated topics like ML algorithms, including a newly added section on generative AI and large language models. You ll also learn about open-source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines before moving on to building an enterprise ML architecture using Amazon Web Services (AWS).In this latest edition, David has updated the entire book to incorporate the latest advancements in science, technology, and solution patterns. The biggest new addition to the handbook is a comprehensive exploration of ML risk management, generative AI, and a deep understanding of the different stages of AI/ML adoption, allowing you to assess your company's position on its AI/ML journeyBy the end of this book, you will have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, technology, real-world solutions architecture, risk management, governance, and the overall AI/ML journey. Moreover, you will possess the skills to design and construct ML solutions and platforms that effectively cater to common use cases and follow established architecture patterns, enabling you to excel as a true professional in the field.What you will learnApply ML methodologies to solve business problemsDesign a practical enterprise ML platform architectureGain a deep understanding of AI risk management frameworks and techniquesBuild an end-to-end data management architecture using AWSTrain large-scale ML models and optimize model inference latencyCreate a business application using AI services and custom modelsDive into generative AI with use cases, architecture patterns, risks, and ethical considerationsWho this book is forThis book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. Also, this book is a great companion for AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management and AI/ML solutions architects who want to expand their scope of knowledge around AI/ML. You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.