Ai Integration In Software Development And Operations

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Ai Integration In Software Development And Operations

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Author : Abhinav Krishna Kaiser
language : en
Publisher: Springer Nature
Release Date : 2024-12-20

Ai Integration In Software Development And Operations written by Abhinav Krishna Kaiser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Computers categories.

Discover how Artificial Intelligence (AI) is transforming the fields of software development, testing, and IT operations by enhancing efficiency, reducing human error, and accelerating processes. This book showcases the practical applications of AI-driven tools, such as automating coding, testing, and operational tasks, predicting potential issues, and optimizing performance. Aimed at digital leaders, practitioners, and customers, this book provides strategic insights and actionable guidance on how to integrate AI technologies to boost productivity, enhance product quality, and streamline development cycles. It serves as a comprehensive guide for those looking to leverage AI to drive innovation, cut costs, and stay competitive in an ever-evolving technological landscape. You’ll explore how AI can be integrated into software development, testing, and IT operations to improve efficiency, accuracy, and speed. Through real-world use cases, you’ll see how AI-driven tools can automate tasks, reduce human error, and improve processes across the development lifecycle. AI Integration in Software Development and Operations offers actionable insights on using AI to accelerate innovation, enhance product quality, and optimize costs in your modern software and IT environments. What You Will Learn Review the SDLC lifecycle, DevOps, SRE and accompanying topics Understand machine learning basics, AI techniques, and data preprocessing for DevOps Explore how AI integration into all phases of SDLC boosts productivity, increases effectiveness, and reduces human error Gain a familiarity with AI tools, their use cases, and the value in integrating them Who This Book is For Software engineers, developers, programmers, DevOps engineers, and AI practitioners who are interested in integrating AI into their DevOps practices.

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.

Synergizing Ai Devops And Deep Learning Integrating Nlp For Next Generation Innovations

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Author : Venkata Mohit Tamanampudi
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2023-04-12

Synergizing Ai Devops And Deep Learning Integrating Nlp For Next Generation Innovations written by Venkata Mohit Tamanampudi and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-12 with Computers categories.

This book explores the powerful intersection of Artificial Intelligence (AI), DevOps, Natural Language Processing (NLP), and Deep Learning, focusing on how these technologies can be combined to build more efficient, automated, and intelligent systems. It delves into the principles behind AI and DevOps, offering a roadmap for integrating these practices to enable continuous delivery and automation of machine learning models. NLP is highlighted as a critical technology that bridges human-computer interaction, while Deep Learning provides the backbone for powerful, data-driven decision-making systems. Readers will gain practical insights into building scalable systems, utilizing AI-driven DevOps pipelines, and integrating NLP for developing smart, interactive applications. The book will provide real-world examples and step-by-step guides for adopting cutting-edge AI/ML methodologies with the speed and agility of DevOps processes, making it an essential read for data scientists, AI engineers, and DevOps practitioners.

Ai Driven Devops Decision Making Transforming Software Development Workflows With Intelligent Systems

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Author : Sumanth Tatineni
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-03-22

Ai Driven Devops Decision Making Transforming Software Development Workflows With Intelligent Systems written by Sumanth Tatineni and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers categories.

This book provides a comprehensive exploration of the "AI-Driven DevOps Decision-Making" system, a groundbreaking approach that leverages artificial intelligence to optimize decision-making processes within DevOps workflows. Drawing upon the foundational research and patent application, the book delves deep into the innovative methodologies, technical frameworks, and real-world applications of this AI-driven system. Beginning with the conceptual underpinnings of DevOps and the challenges of dynamic software development environments, the book outlines how AI can revolutionize traditional workflows by providing real-time insights, predictive analytics, and adaptive learning capabilities. It provides an in-depth understanding of the core components and algorithms of the system, as well as its practical implementation across different stages of the DevOps lifecycle. Each chapter combines theoretical knowledge with practical insights, supported by case studies and examples that illustrate the tangible benefits of adopting this intelligent decision-making framework. The book also envisions the future of AI in DevOps, highlighting opportunities for innovation and further research in optimizing software development practices. By the end of this book, readers will have a thorough understanding of how to integrate AI-driven decision-making tools within their DevOps environments, ultimately leading to more efficient, agile, and reliable software development processes.

Engineering The Future Ai Augmented Devsecops And Cloud Native Platforms For The Enterprise 2025

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Author : Author:1-Chandrakanth Devarakadra Anantha, Author:2-Dr Priyanka Kaushik
language : en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date :

Engineering The Future Ai Augmented Devsecops And Cloud Native Platforms For The Enterprise 2025 written by Author:1-Chandrakanth Devarakadra Anantha, Author:2-Dr Priyanka Kaushik and has been published by RAVEENA PRAKASHAN OPC PVT LTD this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE The rapid evolution of technology has fundamentally altered how enterprises operate, with a significant shift towards cloud-native platforms and AI-powered tools. The convergence of artificial intelligence (AI) and DevSecOps (Development, Security, and Operations) has brought about a new era in enterprise technology, one that emphasizes automation, scalability, and security in every layer of the development lifecycle. “Engineering the Future: AI-Augmented DevSecOps and Cloud-Native Platforms for the Enterprise” explores this transformative intersection, offering a comprehensive guide to understanding and leveraging AI and cloud-native technologies to drive innovation, efficiency, and security within the enterprise ecosystem. At its core, this book delves into how AI can augment DevSecOps practices to foster a more secure, agile, and efficient development pipeline. By integrating AI into the DevSecOps process, organizations can achieve enhanced automation, proactive threat detection, and real-time insights, making it easier to develop and deploy secure applications in increasingly complex cloud environments. AI-powered solutions can detect vulnerabilities, optimize workflows, and automate compliance checks, allowing development teams to focus on innovation without sacrificing security. As businesses embrace cloud-native architectures, where microservices and containerization enable greater flexibility and scalability, the need for AI to facilitate seamless operations across distributed systems becomes ever more critical. The enterprise landscape has witnessed an unprecedented shift towards cloud-first strategies, which have revolutionized the way applications are developed, deployed, and maintained. Cloud-native platforms enable enterprises to accelerate their digital transformation, providing the agility to rapidly scale and innovate while ensuring robust security measures are embedded into every stage of the development lifecycle. Cloud-native technologies, such as Kubernetes, containerization, and serverless architectures, have become essential building blocks for modern enterprise applications. However, with this new paradigm come complex challenges in managing infrastructure, maintaining security, and ensuring smooth integration across diverse environments. This book offers insights into how AI-augmented DevSecOps practices can address these challenges, enabling organizations to stay ahead in an increasingly competitive and fast-paced business world. The synergy between AI and cloud-native platforms is particularly evident in the areas of continuous integration and continuous delivery (CI/CD), where AI-driven tools can enhance deployment efficiency and reduce human errors. By automating repetitive tasks, AI-powered systems free up valuable developer time, allowing them to focus on higher-value activities that directly contribute to business growth. Furthermore, AI’s predictive capabilities enable proactive decision-making, identifying potential bottlenecks, vulnerabilities, or failures before they affect production environments. This is especially important as enterprises adopt multi-cloud and hybrid cloud strategies, where seamless integration, monitoring, and security across various cloud platforms are critical to maintaining operational continuity. Security is at the forefront of every conversation in the world of DevSecOps, particularly as cyber threats become more sophisticated and persistent. AI plays a vital role in strengthening security frameworks by automating threat detection, identifying abnormal patterns, and responding to incidents in real-time. The integration of AI into security processes within DevSecOps workflows helps organizations address vulnerabilities faster and more efficiently, reducing the window of opportunity for attackers. This book examines how AI can enhance traditional security measures, enabling organizations to secure their cloud-native applications against ever-evolving threats. As enterprises continue to evolve in the digital age, the role of AI in augmenting DevSecOps and cloud-native platforms will only grow more pivotal. Organizations that embrace these technologies will be better positioned to innovate at scale while ensuring their applications remain secure and resilient. This book is designed for IT leaders, product managers, developers, and security professionals who are seeking to navigate the complexities of AI, DevSecOps, and cloud-native technologies. Whether you are looking to integrate AI into your DevSecOps pipeline, adopt cloud-native architectures, or enhance your enterprise’s security posture, “Engineering the Future” provides the necessary tools, frameworks, and strategies to succeed in this rapidly evolving landscape. In the pages that follow, you will gain a deeper understanding of how AI can drive automation and intelligence in DevSecOps practices, how cloud-native platforms are transforming enterprise IT operations, and how organizations can seamlessly integrate these technologies to build the secure, scalable, and agile applications of tomorrow. Welcome to the future of enterprise technology—one where AI and cloud-native platforms work hand in hand to drive innovation, security, and operational excellence. Authors

Integrating Artificial Intelligence With Devops

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Author : Sumanth Tatineni
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-03-15

Integrating Artificial Intelligence With Devops written by Sumanth Tatineni and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-15 with Computers categories.

Unlock the future of software development with Integrating Artificial Intelligence with DevOps: Advanced Techniques, Predictive Analytics, and Automation for Real-Time Optimization and Security in Modern Software Development. This comprehensive monograph is a must-read for professionals seeking to revolutionize their DevOps workflows through the power of AI. Dive deep into the intricate integration of Artificial Intelligence within DevOps practices and discover advanced methodologies that enhance every stage of the software development lifecycle. From predictive analytics and intelligent automation to real-time optimization and robust security measures, this book offers a wealth of knowledge for optimizing software delivery. Explore practical applications, in-depth case studies, and best practices that illustrate the transformative potential of AI in DevOps. Each chapter builds on the previous, providing a seamless and cohesive narrative that guides readers through foundational concepts to advanced implementations. Whether you're looking to improve CI/CD pipelines, automate testing and monitoring, manage infrastructure more efficiently, or enhance security with AI-driven techniques, this book equips you with the tools and insights needed to ensure high-quality, secure, and efficient software delivery. Join the vanguard of modern software development with Integrating Artificial Intelligence with DevOps, and harness AI to achieve real-time optimization and unparalleled security in your DevOps processes.

Autonomous Operational And Business Support Systems In Retail Manufacturing Integrating Ai Agentic Intelligence And Scalable Data Engineering

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Author : Shabrinath Motamary
language : en
Publisher: GLOBAL PEN PRESS UK
Release Date :

Autonomous Operational And Business Support Systems In Retail Manufacturing Integrating Ai Agentic Intelligence And Scalable Data Engineering written by Shabrinath Motamary and has been published by GLOBAL PEN PRESS UK this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

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Handbook On Digital Twin And Artificial Intelligence Techniques For Rail Applications

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Author : Maksym Spiryagin
language : en
Publisher: CRC Press
Release Date : 2025-06-17

Handbook On Digital Twin And Artificial Intelligence Techniques For Rail Applications written by Maksym Spiryagin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Technology & Engineering categories.

With contributions from experts from around the world, this handbook aims to systemize the existing experience and knowledge that can be used to the development of more efficient and controlled railway systems. As a result, this handbook showcases the modern methods, methodologies and frameworks for the development of DT and AI architectures and apparatus in the area of the existing railway systems and transport engineering tasks. The chapters cover such varied and specialized topics as the processes related to the transformation of a physical twin into a digital twin; the application of data-driven and physics-based simulation approaches in the development of digital twins; asset management application tasks with the implementation of DT and AI; and the experimental and field applications of the DT and AI concepts and technologies in railway transport system design and predictive maintenance tasks. Handbook on Digital Twin and Artificial Intelligence Techniques for Rail Applications is essential reading for engineers, practitioners and researchers involved in the development of railway transport and transit systems.

Integrating Blue Green Infrastructure Into Urban Development

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Author : Gupta, Shashi Kant
language : en
Publisher: IGI Global
Release Date : 2024-12-05

Integrating Blue Green Infrastructure Into Urban Development written by Gupta, Shashi Kant and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-05 with Business & Economics categories.

The necessity for innovative solutions is critical in the pursuit of a sustainable future, as the rapidly urbanizing world confronts climate change, biodiversity loss, and public health concerns. The concept of blue and green infrastructure is a game-changing innovation. Blue and green infrastructure provides a solution to urban planning by strategically incorporating natural and engineered aquatic features – blue infrastructure – with vegetation and green spaces – green infrastructure. This approach addresses environmental, social, and economic challenges, and improves water quality, reduces urban heat islands, and mitigates flood risks, enhancing urban resilience. It also fosters biodiversity by establishing habitats for a variety of species and establishing green spaces for human recreation and well-being. Further exploration may help establish blue-green infrastructure as a standard in urban development practices. Integrating Blue-Green Infrastructure Into Urban Development investigates the numerous opportunities that integrated infrastructure offers, including increased biodiversity, improved air and water quality, and enhanced climate resilience. It also addresses obstacles, such as stakeholder coordination, funding constraints, and equitable access to green and aquatic spaces. This book covers topics such as smart technology, energy engineering, and cybersecurity, and is a useful resource for architects, civil engineers, business owners, policymakers, environmentalists, scientists, researchers, and academicians.

Ai Enhanced Observability Intelligent Performance Monitoring For Cloud Native Architectures 2025

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Author : Ankush Jitendrakumar Tyagi, Dr. Lalit Kumar
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Ai Enhanced Observability Intelligent Performance Monitoring For Cloud Native Architectures 2025 written by Ankush Jitendrakumar Tyagi, Dr. Lalit Kumar 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 The rapid evolution of cloud-native architecture has reshaped the way applications are designed, developed, and deployed. With the increasing complexity of these systems, traditional methods of performance monitoring and observability have struggled to keep pace. The need for real-time insights, proactive issue detection, and intelligent decision-making has never been more urgent. In this context, AI-enhanced observability emerges as a critical innovation, enabling businesses to leverage artificial intelligence (AI) and machine learning (ML) to transform how they monitor, analyze, and optimize cloud-native applications. The concept of observability is no longer limited to tracking basic metrics such as CPU usage or memory consumption. Instead, it has expanded to include deeper insights into the overall system behavior, user experiences, and distributed systems’ performance. As cloud-native architectures, powered by technologies like microservices, containers, and serverless computing, become more widespread, monitoring and observing every aspect of a system’s performance has become a highly complex and resource-intensive challenge. AI-enhanced observability addresses this complexity by automating and improving the collection, analysis, and interpretation of vast amounts of data generated by cloud-native applications. This book, AI-Enhanced Observability: Intelligent Performance Monitoring for Cloud-Native Architectures, explores the intersection of AI-driven observability and cloud-native systems. It aims to provide readers with an in-depth understanding of how artificial intelligence and machine learning can be harnessed to deliver smarter performance monitoring, detect anomalies faster, and enable better decision-making in cloud-native environments. Through intelligent monitoring and predictive insights, organizations can move from a reactive approach to a proactive one, identifying issues before they affect performance and ultimately improving the reliability, scalability, and efficiency of their systems. The evolution of cloud-native architecture has led to the proliferation of diverse and distributed components, often running in dynamic and highly elastic environments. Traditional tools, which were originally designed for more static, monolithic systems, can no longer handle the volume, velocity, and variety of data required to gain comprehensive visibility into these modern architectures. AI and machine learning technologies offer the promise of transforming observability from a collection of data points into a comprehensive, intelligent system capable of continuously learning from its environment and delivering actionable insights in real-time. This book covers a range of critical topics, including automated anomaly detection, root cause analysis, predictive monitoring, and adaptive alerting, among others. Each of these concepts plays a crucial role in helping organizations monitor the health of their cloud-native applications and infrastructure. The integration of AI allows for the identification of patterns and behaviors that traditional methods may miss, providing more granular insights into system performance and user experience. As cloud-native architecture continues to grow in complexity, leveraging AI to enhance observability will become not just a best practice but a necessity for maintaining the performance and reliability of modern systems. This book is written for cloud architects, site reliability engineers (SREs), DevOps teams, and anyone involved in the development, deployment, and maintenance of cloud-native applications. Whether you are looking to enhance your organization’s ability to monitor performance, identify bottlenecks, or gain predictive insights into your cloud infrastructure, this book will provide valuable insights and actionable strategies to achieve smarter, more efficient observability. The chapters of this book are organized to introduce the fundamental principles of AI-enhanced observability, followed by detailed discussions on how these concepts are applied to real-world scenarios in cloud-native environments. Each chapter is designed to build upon the previous one, with practical examples, case studies, and step-by-step guides to help readers implement AI-driven observability solutions in their own organizations. In addition to exploring the theoretical underpinnings of AI-enhanced observability, this book also provides practical guidance on selecting the right tools, integrating machine learning models into observability platforms, and addressing the challenges that arise when scaling observability practices in large, complex systems. By the end of this book, readers will have a clear understanding of how AI can be leveraged to improve performance monitoring and observability in cloud-native environments, leading to enhanced operational efficiency, reliability, and user satisfaction. I hope that this book provides you with the knowledge and tools to embrace the future of observability, enabling you to stay ahead of challenges, drive innovation, and optimize the performance of your cloud-native applications. Authors