Integrating Artificial Intelligence with DevOps
Integrating Artificial Intelligence with DevOps

Author: Sumanth Tatineni

Publisher: Libertatem Media Private Limited

Published: 2024-03-15

Total Pages: 251

ISBN-13: 8197138214

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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.

Accelerated DevOps with AI, ML & RPA
Accelerated DevOps with AI, ML & RPA

Author: Stephen Fleming

Publisher: Stephen Fleming

Published: 2020-07-14

Total Pages: 100

ISBN-13:

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What comes to your mind after reading the below statements from a renowned industry research firm? It is predicted that a large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. Also, Only 47% of machine learning models are making it into production (Comes MLOPS!) Do you have similar thoughts? Is it just a new Buzzword or repackaging of the existing system? If it’s for real, how is it going to impact the Business/Industry? How my business or job would get impacted? If it has just started, how can I leverage from wherever I am? Which are the major players/startups in this area? Depending on your role, it may be useful for you to know about AIOPS & MLOPS: If you are a Business Consultant trying to make the system more efficient and profitable, reaping the benefits of Automation in your application development process If you are a Technology Consultant and want to make your operation more Agile, Automated and easily deployable If you are a Technology Professional looking for a role in these upcoming areas to be an early adopter in your organization or just starting your career and want to understand the ecosystem If you are from HR or Training field and want to understand the job/Training requirements for these upcoming roles Beyond the apparent hustle and bustle of buzzwords and nomenclature every year, I genuinely believe that AI would drastically change the software development and deployment model in the next two years, and all these new startups would drive this change. It’s astonishing how fast this cycle is moving. Especially for us who had seen the world before the internet came into our daily lives!!This book is my attempt to update you on the unfolding story of AIOPS and MLOPS as “story till now. “ So here is to our Continuous Learning and Progress! Cheers.

Deploying AI in the Enterprise
Deploying AI in the Enterprise

Author: Eberhard Hechler

Publisher: Apress

Published: 2020-09-30

Total Pages: 331

ISBN-13: 9781484262054

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Your company has committed to AI. Congratulations, now what? This practical book offers a holistic plan for implementing AI from the perspective of IT and IT operations in the enterprise. You will learn about AI’s capabilities, potential, limitations, and challenges. This book teaches you about the role of AI in the context of well-established areas, such as design thinking and DevOps, governance and change management, blockchain, and quantum computing, and discusses the convergence of AI in these key areas of the enterprise. Deploying AI in the Enterprise provides guidance and methods to effectively deploy and operationalize sustainable AI solutions. You will learn about deployment challenges, such as AI operationalization issues and roadblocks when it comes to turning insight into actionable predictions. You also will learn how to recognize the key components of AI information architecture, and its role in enabling successful and sustainable AI deployments. And you will come away with an understanding of how to effectively leverage AI to augment usage of core information in Master Data Management (MDM) solutions. What You Will Learn Understand the most important AI concepts, including machine learning and deep learning Follow best practices and methods to successfully deploy and operationalize AI solutions Identify critical components of AI information architecture and the importance of having a plan Integrate AI into existing initiatives within an organization Recognize current limitations of AI, and how this could impact your business Build awareness about important and timely AI research Adjust your mindset to consider AI from a holistic standpoint Get acquainted with AI opportunities that exist in various industries Who This Book Is For IT pros, data scientists, and architects who need to address deployment and operational challenges related to AI and need a comprehensive overview on how AI impacts other business critical areas. It is not an introduction, but is for the reader who is looking for examples on how to leverage data to derive actionable insight and predictions, and needs to understand and factor in the current risks and limitations of AI and what it means in an industry-relevant context.

Practical MLOps
Practical MLOps

Author: Noah Gift

Publisher: "O'Reilly Media, Inc."

Published: 2021-09-14

Total Pages: 461

ISBN-13: 1098102983

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Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

AI-Powered DevOps
AI-Powered DevOps

Author:

Publisher:

Published: 2023-10-10

Total Pages: 0

ISBN-13: 9780645966619

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In an era where technology drives businesses and innovation, the synergy between DevOps, Artificial Intelligence (AI), and Machine Learning (ML) is rewriting the rulebook of software development and operations. "AI-Powered DevOps" is your comprehensive guide to navigating this transformative landscape.Unlock the true potential of DevOps as you explore its philosophy, most popular tools, and groundbreaking integration with AI and ML. Discover how organizations are reaping the benefits of faster delivery, improved collaboration, and reduced costs through real-world case studies from tech giants and startups alike.Dive into the heart of AI-driven DevOps, where predictive analysis keeps systems running smoothly, and intelligent automation takes care of repetitive tasks. Learn how AI enhances security by identifying anomalies and responding to threats in real-time, safeguarding your digital assets like never before. "AI-Powered DevOps" equips you with the knowledge to optimize your systems through dynamic resource allocation, smart alerts, and advanced monitoring. Overcome challenges in adopting this powerful approach, and glimpse into the future where DevOps converges with edge computing and IoT.Whether you're a seasoned DevOps professional or just embarking on your journey, this book is your indispensable companion. Embrace the future of DevOps with AI and ML, and ensure your systems are not only secure but also highly available, efficient, and ready to meet the challenges of tomorrow.Join the DevOps revolution and unleash the potential of your systems with "AI-Powered DevOps" .

Machine Learning Design Patterns
Machine Learning Design Patterns

Author: Valliappa Lakshmanan

Publisher: O'Reilly Media

Published: 2020-10-15

Total Pages: 408

ISBN-13: 1098115759

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The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

Cases on Enhancing Business Sustainability Through Knowledge Management Systems
Cases on Enhancing Business Sustainability Through Knowledge Management Systems

Author: Russ, Meir

Publisher: IGI Global

Published: 2023-06-26

Total Pages: 390

ISBN-13: 1668458616

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Artificial intelligence (AI) is becoming a reality for pioneering organizations while they are facing complex and multifaceted aspects of business sustainability with ambiguous and changing ethical norms and vague or nonexistent legislation. The first quarter of the 21st century was identified as the beginning of the continuous, ongoing, and accelerating wave of simultaneous general purpose technologies revolutions causing accelerated shrinkage of the half-life of knowledge. Cases on Enhancing Business Sustainability Through Knowledge Management Systems presents teaching case studies exploring the formulation and implementation of knowledge management systems (KMS) in organizations. Covering topics such as automation, machine learning, and socio-ecological innovation, this case book is an essential resource for business leaders and managers, IT managers, entrepreneurs, government officials, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

Artificial Intelligence Solutions for Cyber-Physical Systems
Artificial Intelligence Solutions for Cyber-Physical Systems

Author: Pushan Kumar Dutta

Publisher: CRC Press

Published: 2024-09-16

Total Pages: 465

ISBN-13: 1040125166

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Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures. Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.

New Sustainable Horizons in Artificial Intelligence and Digital Solutions
New Sustainable Horizons in Artificial Intelligence and Digital Solutions

Author: Marijn Janssen

Publisher: Springer Nature

Published: 2024-01-15

Total Pages: 439

ISBN-13: 3031500407

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This book constitutes the refereed proceedings of the 22nd IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2023, held in Curitiba, Brazil, during November 9–11, 2023. The 29 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 68 submissions. The contributions were organized in topical sections as follows: Artificial Intelligence and Algorithm; Digital Transformation and New Technologies; and Sustainable Technologies and Smart Cities.

AIxIA 2021 – Advances in Artificial Intelligence
AIxIA 2021 – Advances in Artificial Intelligence

Author: Stefania Bandini

Publisher: Springer Nature

Published: 2022-07-18

Total Pages: 720

ISBN-13: 3031084217

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​This book constitutes revised selected papers from the refereed proceedings of the 20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021, which was held virtually in December 2021. The 36 full papers included in this book were carefully reviewed and selected from 58 submissions; the volume also contains 12 extended and revised workshop contributions. The papers were organized in topical sections as follows: Planning and strategies; constraints, argumentation, and logic programming; knowledge representation, reasoning, and learning; natural language processing; AI for content and social media analysis; signal processing: images, videos and speech; machine learning for argumentation, explanation, and exploration; machine learning and applications; and AI applications.