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Aws Devops For Genai Automating And Scaling Ai Solutions

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Aws Devops For Genai Automating And Scaling Ai Solutions

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Author : Prachi Tembhekar
language : en
Publisher: Prachi Tembhekar
Release Date : 2024-02-15

Aws Devops For Genai Automating And Scaling Ai Solutions written by Prachi Tembhekar and has been published by Prachi Tembhekar this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-15 with Computers categories.

" In "AWS DevOps for GenAI: Automating and Scaling AI Solutions," explore the transformative synergy between DevOps practices and Generative AI (GenAI) within the AWS ecosystem. This comprehensive guide delves into the critical role of AWS DevOps tools in deploying and scaling GenAI applications efficiently. Learn how to set up a robust AWS environment, automate infrastructure with CloudFormation, streamline CI/CD processes with CodeDeploy, and implement comprehensive monitoring with CloudWatch. Integrate AWS SageMaker for end-to-end machine learning workflows while ensuring robust security and compliance. Discover strategies for optimizing performance and managing costs effectively. Through real-world case studies in healthcare, finance, and e-commerce, gain insights into successful implementations and best practices. Stay ahead with future trends and innovations, preparing for the next generation of AI solutions. Whether you're a developer, data scientist, or IT professional, this book equips you with the knowledge and tools to harness the full potential of AWS DevOps for GenAI. ... "

Aws Devops For Genai Automating And Scaling Ai Solutions

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Author : Prachi Tembhekar
language : en
Publisher: Prachi Tembhekar
Release Date : 2024-02-15

Kubernetes For Generative Ai Solutions

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Author : Ashok Srirama
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-06-06

Kubernetes For Generative Ai Solutions written by Ashok Srirama and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.

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.

Combating Cyberattacks Targeting The Ai Ecosystem

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Author : Aditya K. Sood
language : en
Publisher: Stylus Publishing, LLC
Release Date : 2024-10-10

Combating Cyberattacks Targeting The Ai Ecosystem written by Aditya K. Sood and has been published by Stylus Publishing, LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-10 with Computers categories.

This book explores in detail the AI-driven cyber threat landscape, including inherent AI threats and risks that exist in Large Language Models (LLMs), Generative AI applications, and the AI infrastructure. The book highlights hands-on technical approaches to detect security flaws in AI systems and applications utilizing the intelligence gathered from real-world case studies. Lastly, the book presents a very detailed discussion of the defense mechanisms and practical solutions to secure LLMs, GenAI applications, and the AI infrastructure. The chapters are structured with a granular framework, starting with AI concepts, followed by practical assessment techniques based on real-world intelligence, and concluding with required security defenses. Artificial Intelligence (AI) and cybersecurity are deeply intertwined and increasingly essential to modern digital defense strategies. The book is a comprehensive resource for IT professionals, business leaders, and cybersecurity experts for understanding and defending against AI-driven cyberattacks. FEATURES: Includes real-world case studies with detailed examples of AI-centric attacks and defense mechanisms Features hands-on security assessments with practical techniques for evaluating the security of AI systems Demonstrates advanced defense strategies with proven methods to protect LLMs, GenAI applications, and the infrastructure

Enterprise Reinvented Ai Cloud And Data At Scale 2025

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Author : Author:1- Souvari Ranjan Biswal, Author:2-Dr. Nagaraj S
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Enterprise Reinvented Ai Cloud And Data At Scale 2025 written by Author:1- Souvari Ranjan Biswal, Author:2-Dr. Nagaraj S and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE In an era defined by digital disruption, enterprises face a singular imperative: to harness the synergistic power of artificial intelligence, cloud computing, and data at unprecedented scale. “Enterprise Reinvented: AI, Cloud, and Data at Scale” emerges from this landscape as both a strategic manifesto and a practical playbook, guiding leaders, architects, and technologists through the seismic shift from monolithic legacy systems to adaptive, intelligence-driven platforms. Rather than viewing AI, cloud, and data as discrete initiatives, this book treats them as deeply intertwined pillars of business reinvention—each amplifying the others to unlock agility, resilience, and transformative insight. We begin by exploring the tectonic forces reshaping the modern enterprise: the exponential growth of data volumes, the maturation of containerized and serverless cloud architectures, and the democratization of machine learning through open-source frameworks and managed services. In these opening chapters, you will discover how strategic alignment between data governance, platform engineering, and AI-driven innovation sets the stage for truly scalable outcomes—from real-time customer personalization and predictive maintenance to autonomous supply chains and intelligent risk management. Subsequent sections dive into the pragmatic mechanics of building “AI-ready” cloud platforms: designing data fabrics that ensure quality, lineage, and compliance; implementing cloud-native architectures that support burst-to-edge workloads; and establishing ML Ops pipelines for continuous model training, validation, and deployment. Case studies drawn from industries as diverse as manufacturing, financial services, and healthcare illustrate how leading organizations navigate governance, security, and cost-optimization challenges while accelerating time-to-value for analytic and AI use cases. Finally, the book offers a forward-looking perspective on the next frontier: how emerging paradigms—such as distributed AI at the edge, digital twins of business processes, and federated learning ecosystems—will redefine the contours of enterprise scale. We also examine the organizational and cultural shifts required to sustain this transformation: cross-functional “platform teams,” data-literate leadership, and an experimentation mindset that balances rigorous risk management with audacious, data-driven ambition. Authors

Kubernetes For Generative Ai Solutions

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Author : Ashok Srirama
language : en
Publisher:
Release Date : 2025-06-06

Kubernetes For Generative Ai Solutions written by Ashok Srirama and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.

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 Description Generative 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.

Generative Ai With Amazon Bedrock

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Author : Shikhar Kwatra
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-31

Generative Ai With Amazon Bedrock written by Shikhar Kwatra and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-31 with Computers categories.

Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standards Key Features Learn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist Architects Master the core techniques to develop and deploy several AI applications at scale Go beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure. Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock. By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learn Explore the generative AI landscape and foundation models in Amazon Bedrock Fine-tune generative models to improve their performance Explore several architecture patterns for different business use cases Gain insights into ethical AI practices, model governance, and risk mitigation strategies Enhance your skills in employing agents to develop intelligence and orchestrate tasks Monitor and understand metrics and Amazon Bedrock model response Explore various industrial use cases and architectures to solve real-world business problems using RAG Stay on top of architectural best practices and industry standards Who this book is for This book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected.

Mastering Ai And Machine Learning For Devops Engineers

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Author : Shantanu Dalal
language : en
Publisher: Independently Published
Release Date : 2025-01-29

Mastering Ai And Machine Learning For Devops Engineers written by Shantanu Dalal and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-29 with Computers categories.

Mastering AI and Machine Learning for DevOps Engineers - Unlock the Power of AI in DevOps The world of DevOps is evolving rapidly, and Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential tools for automating, optimizing, and scaling modern development and deployment pipelines. Mastering AI and Machine Learning for DevOps Engineers is a comprehensive guide designed to help professionals integrate AI-driven techniques into DevOps workflows, enhancing efficiency, reliability, and innovation. This book bridges the gap between DevOps practices and AI capabilities, providing practical insights, real-world examples, and hands-on techniques that empower engineers, developers, and IT professionals to leverage ML models for automation, predictive analytics, and intelligent decision-making. What You'll Learn in This Book: ✅ Fundamentals of AI & ML for DevOps - Understand key AI/ML concepts and how they apply to automation, monitoring, and infrastructure optimization. ✅ Automating DevOps Pipelines with AI - Learn how AI enhances CI/CD workflows, error detection, and self-healing systems. ✅ Predictive Analytics in DevOps - Use ML models to forecast system failures, optimize resources, and improve software delivery cycles. ✅ AI-Driven Monitoring & Observability - Implement AI-powered tools for log analysis, anomaly detection, and security threat mitigation. ✅ Infrastructure as Code (IaC) with AI - Enhance Terraform, Kubernetes, and cloud automation with intelligent decision-making models. ✅ Machine Learning in Cloud & Edge Computing - Deploy AI models in AWS, Azure, and Kubernetes clusters for efficient DevOps operations. ✅ ChatOps & AI Assistants for DevOps - Integrate AI-driven bots and virtual assistants for faster collaboration and incident response. ✅ Real-World Use Cases & Case Studies - Explore successful AI implementations in DevOps and how leading tech companies leverage ML-driven automation. Who Is This Book For? DevOps Engineers & SREs looking to integrate AI into their automation pipelines. Software Developers & Architects who want to understand how ML enhances modern application development. IT Managers & Cloud Engineers aiming to implement AI-powered monitoring, security, and infrastructure automation. Data Scientists & AI Enthusiasts exploring AI-driven solutions for DevOps challenges. Why This Book?

The Definitive Guide To Machine Learning Operations In Aws

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Author : Neel Sendas
language : en
Publisher: Springer Nature
Release Date : 2025-01-03

The Definitive Guide To Machine Learning Operations In Aws written by Neel Sendas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.

Foreword by Dr. Shreyas Subramanian, Principal Data Scientist, Amazon This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOps tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS. What you will learn: ● Create repeatable training workflows to accelerate model development ● Catalog ML artifacts centrally for model reproducibility and governance ● Integrate ML workflows with CI/CD pipelines for faster time to production ● Continuously monitor data and models in production to maintain quality ● Optimize model deployment for performance and cost Who this book is for: This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.

Automated Machine Learning On Aws

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Author : Trenton Potgieter
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-15

Automated Machine Learning On Aws written by Trenton Potgieter and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-15 with Computers categories.

Automate the process of building, training, and deploying machine learning applications to production with AWS solutions such as SageMaker Autopilot, AutoGluon, Step Functions, Amazon Managed Workflows for Apache Airflow, and more Key FeaturesExplore the various AWS services that make automated machine learning easierRecognize the role of DevOps and MLOps methodologies in pipeline automationGet acquainted with additional AWS services such as Step Functions, MWAA, and more to overcome automation challengesBook Description AWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you'll learn how to automate a machine learning pipeline using the various AWS services. Automated Machine Learning on AWS begins with a quick overview of what the machine learning pipeline/process looks like and highlights the typical challenges that you may face when building a pipeline. Throughout the book, you'll become well versed with various AWS solutions such as Amazon SageMaker Autopilot, AutoGluon, and AWS Step Functions to automate an end-to-end ML process with the help of hands-on examples. The book will show you how to build, monitor, and execute a CI/CD pipeline for the ML process and how the various CI/CD services within AWS can be applied to a use case with the Cloud Development Kit (CDK). You'll understand what a data-centric ML process is by working with the Amazon Managed Services for Apache Airflow and then build a managed Airflow environment. You'll also cover the key success criteria for an MLSDLC implementation and the process of creating a self-mutating CI/CD pipeline using AWS CDK from the perspective of the platform engineering team. By the end of this AWS book, you'll be able to effectively automate a complete machine learning pipeline and deploy it to production. What you will learnEmploy SageMaker Autopilot and Amazon SageMaker SDK to automate the machine learning processUnderstand how to use AutoGluon to automate complicated model building tasksUse the AWS CDK to codify the machine learning processCreate, deploy, and rebuild a CI/CD pipeline on AWSBuild an ML workflow using AWS Step Functions and the Data Science SDKLeverage the Amazon SageMaker Feature Store to automate the machine learning software development life cycle (MLSDLC)Discover how to use Amazon MWAA for a data-centric ML processWho this book is for This book is for the novice as well as experienced machine learning practitioners looking to automate the process of building, training, and deploying machine learning-based solutions into production, using both purpose-built and other AWS services. A basic understanding of the end-to-end machine learning process and concepts, Python programming, and AWS is necessary to make the most out of this book.

Aws Devops For Genai Automating And Scaling Ai Solutions

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