Optimizing Generative Ai Workloads For Sustainability
Download Optimizing Generative Ai Workloads For Sustainability PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Optimizing Generative Ai Workloads For Sustainability book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Optimizing Generative AI Workloads for Sustainability
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
Sustainable Cloud Development
Author: Parth Girish Patel
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-03-28
Reduce cloud costs and carbon footprint with sustainable design, GenAI, and green architecture principles Key Features Discover sustainable cloud practices, including carbon footprint analysis, optimization, and security Explore best practices, insights, and case studies for implementing sustainable solutions like generative AI workloads Learn cost-saving strategies through efficient resource use and business alignment Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by three seasoned AWS solution architects, sustainability mentors, and thought leaders, Sustainable Cloud Development equips cloud professionals with actionable strategies to design, build, and optimize workloads that minimize environmental impact, while maintaining performance and scalability. This book combines practical insights, best practices, and case studies to help you align your cloud operations with global sustainability goals. From foundational concepts such as carbon footprint measurement to advanced techniques such as sustainable software architecture, generative AI lifecycle optimization, and cost-efficient cloud practices, this book covers every aspect of sustainable cloud development. You’ll get to grips with key tools, including AWS Cost Explorer, for analyzing costs and usage over time to right-size deployments; auto scaling for automatically scaling compute resources dynamically based on demand; Amazon Trusted Advisor for reviewing optimization recommendations across critical areas such as cost, performance, and security; and Amazon CloudWatch for detailed monitoring and threshold-based alerting around all resources and applications. This book serves as a practical blueprint for optimizing your cloud workloads for both high performance and a minimal environmental footprint.What you will learn Explore the principles of sustainable cloud computing and application performance analysis Discover best practices for data lifecycle management, storage optimization, and networking efficiency Understand and analyze the carbon footprint of cloud applications Implement sustainable software architecture and coding patterns Optimize the lifecycle and consumption of generative AI models Align cloud services with sustainability goals and global regulations Explore eco-friendly generative AI practices, including efficient model deployment Who this book is for This book is for cloud architects, engineers, DevOps professionals, and IT sustainability specialists who want to align their cloud practices with environmental goals. It also caters to software developers eager to build green, efficient solutions. A basic understanding of cloud services and IT infrastructure is necessary.
Kubernetes for Generative AI Solutions
Author: Ashok Srirama
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-06-06
Master the complete Generative AI project lifecycle on Kubernetes (K8s) from design and optimization to deployment using best practices, cost-effective strategies, and real-world examples. Key Features Build and deploy your first Generative AI workload on Kubernetes with confidence Learn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automation Gain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloads Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGenerative AI (GenAI) is revolutionizing industries, from chatbots to recommendation engines to content creation, but deploying these systems at scale poses significant challenges in infrastructure, scalability, security, and cost management. This book is your practical guide to designing, optimizing, and deploying GenAI workloads with Kubernetes (K8s) the leading container orchestration platform trusted by AI pioneers. Whether you're working with large language models, transformer systems, or other GenAI applications, this book helps you confidently take projects from concept to production. You’ll get to grips with foundational concepts in machine learning and GenAI, understanding how to align projects with business goals and KPIs. From there, you'll set up Kubernetes clusters in the cloud, deploy your first workload, and build a solid infrastructure. But your learning doesn't stop at deployment. The chapters highlight essential strategies for scaling GenAI workloads in production, covering model optimization, workflow automation, scaling, GPU efficiency, observability, security, and resilience. By the end of this book, you’ll be fully equipped to confidently design and deploy scalable, secure, resilient, and cost-effective GenAI solutions on Kubernetes.What you will learn Explore GenAI deployment stack, agents, RAG, and model fine-tuning Implement HPA, VPA, and Karpenter for efficient autoscaling Optimize GPU usage with fractional allocation, MIG, and MPS setups Reduce cloud costs and monitor spending with Kubecost tools Secure GenAI workloads with RBAC, encryption, and service meshes Monitor system health and performance using Prometheus and Grafana Ensure high availability and disaster recovery for GenAI systems Automate GenAI pipelines for continuous integration and delivery Who this book is for This book is for solutions architects, product managers, engineering leads, DevOps teams, GenAI developers, and AI engineers. It's also suitable for students and academics learning about GenAI, Kubernetes, and cloud-native technologies. A basic understanding of cloud computing and AI concepts is needed, but no prior knowledge of Kubernetes is required.