Optimizing Generative AI Workloads for Sustainability: Balancing Performance and Environmental Impact in Generative AI
Dua, Ishneet Kaur, Patel, Parth Girish
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商品描述
This comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible. As advances in Generative AI such as large language models accelerate, optimizing these resource-intensive workloads for efficiency and alignment with human values grows increasingly urgent.
The book starts with the concept of Generative AI and its wide-ranging applications, while also delving into the environmental impact of AI workloads and the growing importance of adopting sustainable AI practices. It then delves into the fundamentals of efficient AI workload management, providing insights into understanding AI workload characteristics, measuring performance, and identifying bottlenecks and inefficiencies. Hardware optimization strategies are explored in detail, covering the selection of energy-efficient hardware, leveraging specialized AI accelerators, and optimizing hardware utilization and scheduling for sustainable operations. You are also guided through software optimization techniques tailored for Generative AI, including efficient model architecture, compression, and quantization methods, and optimization of software libraries and frameworks. Data management and preprocessing strategies are also addressed, emphasizing efficient data storage, cleaning, preprocessing, and augmentation techniques to enhance sustainability throughout the data life cycle. The book further explores model training and inference optimization, cloud and edge computing strategies for Generative AI, energy-efficient deployment and scaling techniques, and sustainable AI life cycle management practices, and concludes with real-world case studies and best practices
By the end of this book, you will take away a toolkit of impactful steps you can implement to minimize the environmental harms and ethical risks of Generative AI. For organizations deploying any type of generative model at scale, this essential guide provides a blueprint for developing responsible AI systems that benefit society.
What You Will Learn
- Understand how Generative AI can be more energy-efficient through improvements such as model compression, efficient architecture, hardware optimization, and carbon footprint tracking
- Know the techniques to minimize data usage, including evaluation, filtering, synthesis, few-shot learning, and monitoring data demands over time
- Understand spanning efficiency, data minimization, and alignment for comprehensive responsibility
- Know the methods for detecting, understanding, and mitigating algorithmic biases, ensuring diversity in data collection, and monitoring model fairness
Who This book Is For
Professionals seeking to adopt responsible and sustainable practices in their Generative AI work; leaders and practitioners who need actionable strategies and recommendations that can be implemented directly in real-world systems and organizational workflows; ML engineers and data scientists building and deploying Generative AI systems in industry settings; and researchers developing new generative AI techniques, such as at technology companies or universities
商品描述(中文翻譯)
這本綜合指南提供了優化生成式人工智慧系統以實現更可持續和負責任的實用策略。隨著生成式人工智慧（如大型語言模型）的進步加速，優化這些資源密集型工作負載以提高效率並與人類價值觀保持一致變得愈加迫切。
本書首先介紹生成式人工智慧的概念及其廣泛的應用，並深入探討人工智慧工作負載對環境的影響以及採用可持續人工智慧實踐日益重要的原因。接著，書中深入探討高效的人工智慧工作負載管理基礎，提供有關理解人工智慧工作負載特徵、測量性能以及識別瓶頸和低效率的見解。硬體優化策略將詳細探討，包括選擇節能硬體、利用專用的人工智慧加速器，以及優化硬體利用率和排程以實現可持續運作。您還將學習針對生成式人工智慧的軟體優化技術，包括高效的模型架構、壓縮和量化方法，以及軟體庫和框架的優化。數據管理和預處理策略也將被討論，強調高效的數據儲存、清理、預處理和增強技術，以提升整個數據生命週期的可持續性。本書進一步探討模型訓練和推理優化、生成式人工智慧的雲端和邊緣計算策略、節能的部署和擴展技術，以及可持續的人工智慧生命週期管理實踐，並以實際案例研究和最佳實踐作結。
在本書結束時，您將獲得一套可實施的工具，幫助您最小化生成式人工智慧對環境的危害和倫理風險。對於在大規模部署任何類型生成模型的組織而言，這本必備指南提供了一個開發負責任的人工智慧系統的藍圖，旨在造福社會。
您將學到的內容包括：
- 了解如何通過模型壓縮、高效架構、硬體優化和碳足跡追蹤等改進，使生成式人工智慧更具能源效率
- 知道最小化數據使用的技術，包括評估、過濾、合成、少量學習以及隨時間監控數據需求
- 理解全面責任的範圍效率、數據最小化和對齊
- 知道檢測、理解和減輕算法偏見的方法，確保數據收集的多樣性，並監控模型的公平性
本書適合於：
尋求在生成式人工智慧工作中採用負責任和可持續實踐的專業人士；需要可直接在現實系統和組織工作流程中實施的可行策略和建議的領導者和從業者；在行業環境中構建和部署生成式人工智慧系統的機器學習工程師和數據科學家；以及在科技公司或大學開發新生成式人工智慧技術的研究人員。
作者簡介
Ishneet Kaur Dua is an experienced solutions architect specializing in generative artificial intelligence, machine learning, environmental sustainability, and cloud computing. With years of hands-on experience, she excels in designing resource efficient, cost-effective, resilient systems on leading cloud platforms such as AWS, GCP, and Azure. Ishneet started her career at CDK Global where she worked as a DevOps engineer and focused on building highly available Kubernetes environments on AWS cloud and on-prem. Passionate about leveraging AI and ML for innovation, Ishneet has expertise in diverse areas, including low code no code ML, computer vision, NLP, recommendation engines, and predictive analytics. She advocates for ethical AI practices, ensuring fairness and transparency in AI systems while making them accessible through open-source initiatives.
As a thought leader, Ishneet shares her insights at global tech conferences, focusing on AI/ML, cloud architecture, and sustainability. She actively mentors women in tech, aiming to inspire and empower the next generation of STEM professionals. Driven by a vision of harnessing technology for positive change, Ishneet is dedicated to building a future where AI creates opportunities for all and addresses complex real-world challenges.
Parth Girish Patel is a seasoned architect with a wealth of experience spanning over 17 years, encompassing management consulting and cloud computing. Currently, at Amazon Web Services (AWS), he specializes in artificial intelligence/machine learning, generative AI, sustainability, application modernization, and cloud-native patterns to deliver resilient, high-performance solutions optimized for cost and operational efficiency.
Starting his career as a software engineer, Parth transitioned into consulting at Deloitte, where he provided strategic guidance to Fortune companies on their cloud implementation and led intricate enterprise transformations. This diverse background equipped him with a unique blend of business acumen and technical expertise, enabling him to navigate complex digital transformations effectively. As an AWS solutions architect, Parth plays a pivotal role in guiding customers through their cloud journey and AI adoption, offering insights into scalable architectures and implementing end-to-end machine learning solutions. With specialization across leading cloud providers like AWS, Azure, and GCP, as well as proficiency in Machine Learning skills like Natural Language Processing, Computer Vision, and predictive analytics, Parth is well-equipped to tackle diverse technical challenges.
Passionate about sustainable AI, Parth advocates for the responsible and ethical use of AI, emphasizing transparency and environmental consciousness. He leverages his leadership skills to mentor teams and individuals, fostering a collaborative and innovative environment aimed at driving a positive impact across organizations and society as a whole.
作者簡介(中文翻譯)
Ishneet Kaur Dua 是一位經驗豐富的解決方案架構師，專注於生成式人工智慧、機器學習、環境可持續性和雲端計算。擁有多年的實務經驗，她在設計資源高效、成本效益佳、具韌性的系統方面表現出色，並在 AWS、GCP 和 Azure 等主要雲端平台上工作。Ishneet 的職業生涯始於 CDK Global，擔任 DevOps 工程師，專注於在 AWS 雲端和本地環境中構建高可用性的 Kubernetes 環境。她熱衷於利用 AI 和 ML 進行創新，並在低代碼無代碼機器學習、計算機視覺、自然語言處理、推薦引擎和預測分析等多個領域擁有專業知識。她提倡道德 AI 實踐，確保 AI 系統的公平性和透明度，同時通過開源倡議使其更具可及性。
作為一位思想領袖，Ishneet 在全球科技會議上分享她的見解，專注於 AI/ML、雲端架構和可持續性。她積極指導科技領域的女性，旨在激勵和賦能下一代 STEM 專業人士。Ishneet 以利用科技促進積極變革為願景，致力於建立一個 AI 為所有人創造機會並解決複雜現實挑戰的未來。
Parth Girish Patel 是一位資深架構師，擁有超過 17 年的豐富經驗，涵蓋管理諮詢和雲端計算。目前在 Amazon Web Services (AWS) 工作，他專注於人工智慧/機器學習、生成式 AI、可持續性、應用現代化和雲原生模式，以提供韌性高效能的解決方案，並優化成本和運營效率。
Parth 的職業生涯始於軟體工程師，隨後轉入 Deloitte 擔任顧問，為財富 500 強公司提供雲端實施的戰略指導，並領導複雜的企業轉型。這樣多樣的背景使他具備獨特的商業敏銳度和技術專業知識，使他能有效應對複雜的數位轉型。作為 AWS 解決方案架構師，Parth 在指導客戶的雲端旅程和 AI 採用方面扮演關鍵角色，提供可擴展架構的見解並實施端到端的機器學習解決方案。憑藉在 AWS、Azure 和 GCP 等主要雲端供應商的專業知識，以及在自然語言處理、計算機視覺和預測分析等機器學習技能方面的熟練，Parth 能夠應對各種技術挑戰。
Parth 對可持續 AI 充滿熱情，提倡負責任和道德的 AI 使用，強調透明度和環境意識。他利用自己的領導能力指導團隊和個人，促進協作和創新的環境，旨在對組織和整個社會產生積極影響。
