{"product_id":"platform-engineering-concepts-challenges-and-applications-hardback-9781394395880","title":"Platform Engineering; Concepts, Challenges and Applications (Hardback) 9781394395880","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003ePlatform Engineering\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eConcepts, Challenges and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eK. Suresh Kumar (Edited by), KS Kumar (Author), S. Sundaresan (Edited by), R. Prithiviraj (Edited by), T. Ananth Kumar (Edited by), S. Balamurugan (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394395880, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 29 June 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e512 pages\u003cbr\u003e22.9 x 15.2 x 3.1 cm, 0.829 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003ePlatform Engineering: Concepts, Challenges and Applications\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eStay at the leading edge of modern infrastructure with this definitive guide to integrating AI, blockchain, and cloud-native methodologies into robust and secure software platforms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAs enterprises across industries adopt cloud-native technologies, DevOps practices, AI-driven tools, and secure infrastructure models, platform engineering has become essential to streamlining development workflows, enhancing developer experience, and improving operational efficiency. This book is a comprehensive and timely exploration of the emerging discipline that is redefining how modern software systems are designed, developed, deployed, and scaled.\u003c\/p\u003e \u003cp\u003eThis edited volume brings together contributions from leading researchers and practitioners to offer a rich blend of theoretical foundations, design strategies, implementation methodologies, and real-world use cases. Covering a wide array of topics—from developer productivity, scalable platform design, and DevOps transformation to advanced technologies like deep learning, reinforcement learning, blockchain, and IoT—the book addresses both the opportunities and challenges involved in building robust, reusable, and secure software platforms. By presenting a structured and insightful view into this rapidly growing field, the book not only serves as a reference guide but also as a source of inspiration for innovation in platform design and deployment across diverse sectors.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e\u003cbr\u003eSoftware engineers, DevOps specialists, platform engineers, cloud architects, security experts, technology leaders, and data scientists mastering modern software delivery, leveraging artificial intelligence and machine learning for building scalable and secure systems.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eSeries Preface xxi\u003cbr\u003ePreface xxiii\u003cbr\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Core Concepts and Evolution of Platform Engineering 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Empowering Developer Productivity through Platform Engineering: A Transformative Approach to Scalable and Streamlined Software Development 3\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eC. Saranya Jothi, E. Surya, M. Syed Rabiya, B. Lalitha and R. Roselinkiruba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction to Platform Engineering 4\u003cbr\u003e1.2 Core Components of a Platform Engineering Strategy 6\u003cbr\u003e1.3 Automation and Its Role in Enhancing Productivity 8\u003cbr\u003e1.4 Impact on Developer Experience 11\u003cbr\u003e1.5 Quantifying Developer Productivity through Platform Engineering 12\u003cbr\u003e1.6 Future Trends and Opportunities in Platform Engineering 14\u003cbr\u003e1.7 Conclusion 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Best Practices for Building Scalable Software Platforms 19\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSwetha S. and Joe Prathap P. M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 19\u003cbr\u003e2.2 Goals and Expectations 20\u003cbr\u003e2.3 Basic Key Metrics 22\u003cbr\u003e2.4 Cloud Computing as Infrastructure 24\u003cbr\u003e2.5 Microservice Architecture 25\u003cbr\u003e2.6 Choosing the Right Database Solution 26\u003cbr\u003e2.7 Effective Scaling Methods 28\u003cbr\u003e2.8 Conclusion 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 The Role of Platform Engineering in DevOps Transformation—The Future of Software Delivery: How Platform Engineering Transforms DevOps 33\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eS. Sri Devi, T. Sangeetha and Olukayode A. Oki\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 34\u003cbr\u003e3.2 Benefits of Platform Engineering 36\u003cbr\u003e3.3 Role and Responsibilities of Platform Engineers 37\u003cbr\u003e3.4 Tools\/Technology in Platform Engineering 37Contents vii\u003cbr\u003e3.5 The Origin Story of DevOps 38\u003cbr\u003e3.6 Extending Agile to the Full Life Cycle 39\u003cbr\u003e3.7 Difference Between Software Engineer and DevOps Engineer 40\u003cbr\u003e3.8 The Role of SDLC in DevOps 42\u003cbr\u003e3.9 Top Programming and Scripting Languages for DevOps 44\u003cbr\u003e3.10 Choosing the Optimal Operating System for DevOps: Linux, Windows, or MacOS 45\u003cbr\u003e3.11 What is Command Line Interface? 46\u003cbr\u003e3.12 What is a Shell? 47\u003cbr\u003e3.13 Networking and Its Role in DevOps 48\u003cbr\u003e3.14 Exploring Infrastructure as Code (IaC): Automation, Scalability, and Efficiency in DevOps 51\u003cbr\u003e3.15 Conclusion 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The Impact of Platform Engineering on Developer Productivity 59\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Dhivya and Matthew Olusegun Adigun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 59\u003cbr\u003e4.2 Key Principles and Components 65\u003cbr\u003e4.3 Comparison with Platform Engineering, Traditional DevOps, and SRE 68viii Contents\u003cbr\u003e4.4 Platform Engineering Centralizes Tooling and Infrastructure 71\u003cbr\u003e4.5 Developer Productivity: Key Metrics and Challenges 75\u003cbr\u003e4.6 Common Productivity Bottlenecks in Software Development 78\u003cbr\u003e4.7 Boosting Developer Productivity 82\u003cbr\u003e4.8 Self-Service Developer Portals and Internal Platforms 83\u003cbr\u003e4.9 Examples of Companies that Successfully Adopted Platform Engineering 87\u003cbr\u003e4.10 Proposed Project: \"Boosting Developer Productivity through Platform Engineering: A Practical Exploration with Internal Developer Platforms\" 90\u003cbr\u003e4.11 Conclusion 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Platform Engineering for Specific Technologies and Architectures 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Platform Engineering for Cloud-Native Applications 99\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAmanpreet Singh, Rupinder Singh and Jaswinder Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 100\u003cbr\u003e5.2 Cloud-Native Applications 101\u003cbr\u003e5.3 Literature Review 103\u003cbr\u003e5.4 Cloud-Native Application Advantages 112\u003cbr\u003e5.5 Tools and Technologies 115\u003cbr\u003e5.6 Areas of Challenge in Platform Engineering 119\u003cbr\u003e5.7 Challenges and Considerations 121\u003cbr\u003e5.8 Conclusion 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Platform Engineering for Cloud-Native Applications: Strategies for Scalable, Cost-Effective, and Automated Cloud Adoption 133\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eP. Divya, R. Parthiban, S. Jayalakshmi and R. Rajmohan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 134\u003cbr\u003e6.2 Core Principles of Platform Engineering 134\u003cbr\u003e6.3 Programming for Productivity and Networking 137\u003cbr\u003e6.4 Continuous Integration and Continuous Deployment (CI\/CD) 138\u003cbr\u003e6.5 Containerization and Orchestration 139x Contents\u003cbr\u003e6.6 Cloud-Native Platform Stack 141\u003cbr\u003e6.7 Protocols in Building and Managing Cloud-Native Platform 145\u003cbr\u003e6.8 Tools and Technology in Platform Engineering 147\u003cbr\u003e6.9 Challenges in Platform Engineering for Cloud-Native Applications 151\u003cbr\u003e6.10 Best Practices in Platform Engineering for Cloud-Native Applications 154\u003cbr\u003e6.11 Performance Analysis 155\u003cbr\u003e6.12 Future Trends 157\u003cbr\u003e6.13 Conclusion 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Optimization Techniques Hybridized into Deep Learning Models 161\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Pathmapriya and Joe Prathap P. M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 162\u003cbr\u003e7.2 Research Prospects 163\u003cbr\u003e7.3 Deep Learning Technique for Diagnosis of Syndrome 168\u003cbr\u003e7.4 Meta-Heuristics Algorihms in Medical Diagnosis 170\u003cbr\u003e7.5 Data Synthesis 177\u003cbr\u003e7.6 Deploying Healthcare Solutions 179\u003cbr\u003e7.7 Discussion and Result 180\u003cbr\u003e7.8 Challenges and Future Direction 181\u003cbr\u003e7.9 Conclusion and Future Research Ideas 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Machine Learning and Automation in Platform Engineering: Transforming Monitoring, Scaling, and Self-Healing 189\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. Roselinkiruba, Vasumathy M., J. Jude Moses Anto Devakanth, C. Saranya Jothi, J. Kavitha and L. Sharmila\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 190Contents xi\u003cbr\u003e8.2 Proposed Methodology 194\u003cbr\u003e8.3 Case Study and Practical Examples 201\u003cbr\u003e8.4 Experimental Results and Analysis 203\u003cbr\u003e8.5 Conclusion and Future Work 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 An Investigative Analysis on Security Challenges in Cloud Computing Models and Solutions 215\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eN.A. Natraj, B. Sundaravadivazhagan, Giri. G. Hallur and Supriya Shrikant Laykar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 216\u003cbr\u003e9.2 Literature Review 220\u003cbr\u003e9.3 Research Methodology 224\u003cbr\u003e9.4 Security Challenges and Solutions in Cloud Computing Models 233\u003cbr\u003e9.5 Quantitative Analysis and Findings 244\u003cbr\u003e9.6 Conclusion 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Applying Ensemble Deep Learning for Enhanced Security in Platform Engineering 255\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. Saranya, S.S. Uma, T.S. Sivarani, Naveena A. Priyadharsini and Sunday Adeola Ajagbe\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 256\u003cbr\u003e10.2 Related Works 258\u003cbr\u003e10.3 Proposed Methodology 261\u003cbr\u003e10.4 Experimental Result and Discussion 272\u003cbr\u003e10.5 Conclusion and Future Work 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Application-Specific Platforms and Emerging Technologies 287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Reinforcement Learning–Driven Secure and Energy-Efficient Transmission Framework for Scalable Platform Engineering 289\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFemila. L., S.P. Subotha, Lavanya Devi. N. and J. Arul King\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Overview 290\u003cbr\u003e11.2 Background 294\u003cbr\u003e11.3 Approach\/Methodology 298\u003cbr\u003e11.4 Results and Discussion 303\u003cbr\u003e11.5 Conclusion 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Platform Engineering for Scalable AI Deployments in Healthcare: Enabling Automated Skin Blemish Detection 309\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eP. Kalpana, T. Sangeetha, S. Siamala Devi and Morenikeji E. Coker\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 310\u003cbr\u003e12.2 Related Work 313\u003cbr\u003e12.3 Modules 315\u003cbr\u003e12.4 Methodology 318\u003cbr\u003e12.5 Experiments 324\u003cbr\u003e12.6 Performance Analysis 328\u003cbr\u003e12.7 Conclusion 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 AI-Driven Platform Engineering for Skin Disease Diagnosis: A Comparative Study of DenseNet Architectures 333\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. Karthick Manoj, S. Aasha Nandhini and M. Batumalay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 334\u003cbr\u003e13.2 Literature Survey 335\u003cbr\u003e13.3 Methodology 339\u003cbr\u003e13.4 Result and Discussion 343\u003cbr\u003e13.5 Conclusion 350\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 An Overview of Current Advances in Blockchain Technology, Platform Engineering, and DevOps and Their Implications 353\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBalaji Ganesh R., Deebalakshmi R. and R. Thilagavathy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 354\u003cbr\u003e14.2 Literature Review 355\u003cbr\u003e14.3 Characteristic of Blockchain 358\u003cbr\u003e14.4 Types of Blockchain 366\u003cbr\u003e14.5 Blockchain Platforms 368\u003cbr\u003e14.6 Blockchain Products 370\u003cbr\u003e14.7 Limitations of the Block Chain 373\u003cbr\u003e14.8 Platform Engineering 378\u003cbr\u003e14.9 DevOps in the Blockchain Industry 380\u003cbr\u003e14.10 Results and Discussion 382\u003cbr\u003e14.11 Conclusion 386\u003cbr\u003e14.12 Future Work 387\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 A Multifaceted Approach to Lung Cancer Detection and Segmentation: Platforms, Algorithms, and Emerging Technologies 391\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eS.S. Uma, S.N. Sindhu Bairavi, R. Saranya, J. Assis Nevatha and Olusola Kunle Akinde\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 392\u003cbr\u003e15.2 Literature Survey 394\u003cbr\u003e15.3 Proposed System 395\u003cbr\u003e15.4 Result and Discussion 406\u003cbr\u003e15.5 Conclusion 416\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 An AI-Augmented IoT System for Small-Scale Cold Chain Applications 419\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDivya James and T.K.S. Lakshmi Priya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 420\u003cbr\u003e16.2 Cold Chains 421\u003cbr\u003e16.3 MSME's in Cold Chain 424\u003cbr\u003e16.4 Need for an Architecture 426\u003cbr\u003e16.5 Proposed Architecture 428\u003cbr\u003e16.6 Experimental Evaluations 431\u003cbr\u003e16.7 Application of AI in Cold Chain Systems 441\u003cbr\u003e16.8 Quantitative Analysis of AI for IoT-Enabled Cold Chain Management 444\u003cbr\u003e16.9 Conclusion 450\u003cbr\u003eBibliography 450\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 A Data-Driven Framework for Crop Price Prediction Using ML, Statistical, and Hybrid Ensemble Models 455\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eManimegalai R., Logendar G., Srirengapriya G. and Ayesha S.K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 456\u003cbr\u003e17.2 Literature Survey 457\u003cbr\u003e17.3 Methodologies 460\u003cbr\u003e17.4 Experimental Results 466\u003cbr\u003e17.5 Conclusions and Future Work 474\u003c\/p\u003e \u003cp\u003eReferences 475\u003cbr\u003eIndex 477\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433833066776,"sku":"9781394395880","price":157.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394395880.jpg?v=1784854809","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/platform-engineering-concepts-challenges-and-applications-hardback-9781394395880","provider":"Freshly Printed Books","version":"1.0","type":"link"}