Scalable Artificial Intelligence Systems Cloud Native Edge Ai Mlops And Governance For Real World Deployment
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Scalable Artificial Intelligence Systems Cloud Native Edge Ai Mlops And Governance For Real World Deployment
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Author : Swarup Panda
language : en
Publisher: Deep Science Publishing
Release Date : 2025-07-28
Scalable Artificial Intelligence Systems Cloud Native Edge Ai Mlops And Governance For Real World Deployment written by Swarup Panda and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-28 with Computers categories.
Artificial Intelligence (AI) has become essential across industries, transforming operations, decision-making, and value creation. As organizations worldwide use AI to address challenges in areas like healthcare, finance, cybersecurity, manufacturing, and infrastructure, the need for reliable and scalable AI systems continues to grow. This book offers practical guidance for professionals designing and deploying scalable, compliant AI solutions in production environments. It covers modernizing legacy systems, building MLOps pipelines, and addressing ethical aspects of autonomous AI, providing essential insights and patterns for real-world applications. We cover essential topics for enterprise AI success, such as scalable architectures (cloud-native, edge, hybrid), MLOps for lifecycle management, and governance for compliance and fairness. The text also outlines frameworks for explainable and federated AI in regulated fields, supporting privacy and distributed intelligence. We demonstrate AI's impact on diagnostics, fraud detection, threat intelligence, and urban planning through case studies, and review how platforms like Azure, AWS, and GCP support scalable AI deployment. This book highlights the need for ethical AI that upholds human values, privacy, and transparency. As AI shapes society, we must design, deploy, and govern it responsibly. I invite you to explore these chapters with a mindset of both innovation and accountability—as together, we shape a future powered by intelligent and responsible systems.
Building Cloud Native Ai And Mlops Platforms For Scalable Secure And Mission Critical Intelligence Systems
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Author : Phanish Lakkarasu
language : en
Publisher: AQUA PUBLICATIONS
Release Date :
Building Cloud Native Ai And Mlops Platforms For Scalable Secure And Mission Critical Intelligence Systems written by Phanish Lakkarasu and has been published by AQUA PUBLICATIONS this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
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Scalable Ai And Design Patterns
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Author : Abhishek Mishra
language : en
Publisher: Springer Nature
Release Date : 2024-03-11
Scalable Ai And Design Patterns written by Abhishek Mishra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-11 with Computers categories.
Understand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation. This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies. The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements. What You Will Learn Develop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processing Know the significance of design patterns in creating robust intelligent solutions Understand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systems Be aware of the ethical implications of scalable AI systems Who This Book Is For AI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience
Designing Scalable And Intelligent Cloud Architectures An End To End Guide To Ai Driven Platforms Mlops Pipelines And Data Engineering For Digital Transformation
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Author : Phanish Lakkarasu
language : en
Publisher: Deep Science Publishing
Release Date : 2025-06-06
Designing Scalable And Intelligent Cloud Architectures An End To End Guide To Ai Driven Platforms Mlops Pipelines And Data Engineering For Digital Transformation written by Phanish Lakkarasu and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.
In today’s fast-paced digital era, organizations are under constant pressure to innovate, scale, and deliver intelligent services with speed and reliability. Designing Scalable and Intelligent Cloud Architectures: An End-to-End Guide to AI-Driven Platforms, MLOps Pipelines, and Data Engineering for Digital Transformation is a comprehensive exploration into the foundational and advanced components required to build robust, future-ready cloud ecosystems. This book is the product of years of observing the shifting paradigms in enterprise IT—from legacy systems and monolithic architectures to microservices, serverless computing, and AI-powered infrastructure. At the heart of this evolution lies the need for cloud-native platforms that are not only scalable and resilient but also intelligent and automation-ready. The content in these pages is aimed at architects, engineers, data scientists, DevOps professionals, and digital transformation leaders who seek to understand and implement the key building blocks of modern cloud systems. It delves into the design principles behind scalable infrastructure, best practices for integrating AI and Machine Learning, and the implementation of MLOps pipelines to streamline deployment, monitoring, and continuous improvement of ML models. Furthermore, it provides practical insights into data engineering strategies that ensure secure, efficient, and real-time data flow across distributed environments. We also explore critical topics such as multi-cloud and hybrid cloud strategies, edge computing, observability, cost optimization, and governance—ensuring that readers are equipped to tackle both the technical and operational challenges of building next-generation platforms. What sets this book apart is its unified approach to cloud, AI, and data engineering—treating them not as isolated silos but as interconnected pillars of intelligent digital transformation. Whether you are designing enterprise-grade solutions or modernizing existing infrastructures, this guide will serve as your companion in navigating complexity with clarity and confidence.
Ai Native Digital Platforms Architecting The Next Wave Of Commerce And Intelligence 2025
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Author : Author:1-Amit Ojha, Author:2-Mr. Kumar Pal Singh
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Ai Native Digital Platforms Architecting The Next Wave Of Commerce And Intelligence 2025 written by Author:1-Amit Ojha, Author:2-Mr. Kumar Pal Singh 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 age where artificial intelligence is no longer a futuristic concept but a fundamental driver of digital transformation, the convergence of AI and platform architecture is redefining how we innovate, interact, and transact. The rise of AI-native digital platforms marks a profound shift in technological design, one that transcends traditional boundaries and paves the way for adaptive, intelligent, and autonomous systems capable of responding to evolving market demands in real time. This book, AI-Native Digital Platforms: Architecting the Next Wave of Commerce and Intelligence, is an exploration of the emerging paradigm in which artificial intelligence is embedded at the very core of digital platform design. It aims to offer a comprehensive framework for understanding how AI reshapes platform architecture—across data pipelines, microservices, edge computing, cloud orchestration, API ecosystems, and user experience personalization. From dynamic supply chains to intelligent customer engagement, AI-native platforms are becoming the backbone of future-ready enterprises. We begin with foundational concepts of digital platforms and AI technologies, then progress through architectural principles, design patterns, case studies, and strategic implementation models. The book addresses key challenges such as ethical AI governance, scalability, interoperability, security, and data sovereignty, all while emphasizing real-world applications across e-commerce, fintech, healthcare, smart cities, and industrial automation. Intending for technology leaders, architects, data scientists, product strategists, and academic researchers, this book bridges the gap between theoretical AI capabilities and practical platform engineering. It reflects a multi-disciplinary perspective shaped by innovations in machine learning, distributed systems, human-centered design, and business intelligence. As AI-native platforms become central to global digital economies, the decisions we make today will influence the societal, ethical, and economic fabric of tomorrow. This book is a guide and a call to action—for building platforms that are not only intelligent but also inclusive, sustainable, and resilient. Authors
Architecting Data And Machine Learning Platforms
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Author : Marco Tranquillin
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-10-12
Architecting Data And Machine Learning Platforms written by Marco Tranquillin and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-12 with Computers categories.
All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach
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.
Artificial Intelligence For Cloud Native Software Engineering
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Author : Chelliah, Pethuru Raj
language : en
Publisher: IGI Global
Release Date : 2025-05-07
Artificial Intelligence For Cloud Native Software Engineering written by Chelliah, Pethuru Raj and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Computers categories.
Artificial intelligence is transforming software engineering by automating development, testing, deployment, and security processes, leading to more efficient and high-quality software solutions. AI-powered tools enhance scalability, reliability, and real-time analytics, enabling businesses to optimize operations and improve decision-making. As cloud-native architectures gain traction, AI-driven innovations are reshaping the way software is designed, maintained, and evolved, driving a new era of intelligent and adaptive technology solutions. Artificial Intelligence for Cloud-Native Software Engineering explores the transformative impact of AI on the software engineering lifecycle, highlighting its role in automating and enhancing various stages of software development. It provides a comprehensive overview of how AI technologies can assist software architects and engineers in creating high-quality, enterprise-grade software efficiently. Covering topics such as source code creation, data security, and multiparameter optimization, this book is an excellent resource for software engineers, computer scientists, professionals, researchers, scholars, academicians, and more.
Building Scalable Edge Ai Solutions For The Internet Of Things
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Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Building Scalable Edge Ai Solutions For The Internet Of Things written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Building Scalable Edge AI Solutions for the Internet of Things" explores the convergence of IoT, edge computing, and AI, providing a practical guide to developing and deploying intelligent edge solutions. The book begins by establishing foundational concepts, explaining the limitations of cloud-centric IoT architectures and introducing the benefits of edge computing and Edge AI, such as reduced latency, bandwidth efficiency, and enhanced privacy. It then delves into the building blocks of Edge AI solutions, covering data acquisition and preprocessing techniques optimized for resource-constrained devices, model selection and compression strategies, and an overview of relevant frameworks and hardware platforms like MCUs, MPUs, GPUs, and FPGAs. The book further provides a practical development approach, detailing the steps from problem definition and data preparation to model training, evaluation, and deployment on edge devices. It emphasizes the importance of robust deployment strategies, including OTA updates, device management tools, and crucial security considerations. Finally, the book examines advanced topics like real-time Edge AI applications in industrial automation, robotics, and autonomous systems, along with scalability and orchestration strategies for large deployments. It concludes by exploring future trends, including emerging hardware and software, the role of 5G, and the ethical implications of Edge AI, advocating for responsible AI development.
Learn Vertex Ai
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-06-27
Learn Vertex Ai written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-27 with Business & Economics categories.
LEARN VERTEX AI Implement Enterprise AI on Google Cloud This book is aimed at technology professionals, data engineers, and students who want to master the use of Vertex AI for creating, automating, and governing artificial intelligence projects in corporate Google Cloud environments. Learn how to structure machine learning pipelines, integrate data, automate deployment and versioning processes, monitor performance, and implement MLOps and DataOps practices with security, scalability, and compliance. Explore practical integrations with BigQuery, Dataflow, Pub/Sub, Cloud Storage, as well as leading frameworks such as TensorFlow, PyTorch, and scikit-learn. Develop skills in multi-cloud deployment, model tuning, cost control, CI/CD automation, and complete governance of the data and model lifecycle. • Professional setup of Vertex AI on Google Cloud • Building automated and scalable machine learning pipelines • Advanced integration with BigQuery, Dataflow, Pub/Sub, and Cloud Storage • Deployment, versioning, and monitoring of production models • Orchestration with TensorFlow, PyTorch, scikit-learn, AutoML, and containers • CI/CD automation, performance tuning, cost control • Implementation of Feature Store, Model Registry, and access policies • Governance, auditing, compliance, and data security in AI • MLOps, DataOps strategies, and multi-cloud integration • Real-world applications, certification preparation, and critical projects Master Vertex AI and become a reference in corporate AI, delivering scalable, auditable projects aligned with global market demands. vertex ai, google cloud, machine learning, nvidia, pipelines, automation, bigquery, dataflow, pub/sub, cloud storage, ci/cd, mlops, automl, tensorflow, pytorch, feature store, model registry, dataops, model deployment, orchestration, monitoring, governance, data security