Microservices For Machine Learning
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Machine Learning In Microservices
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Author : Mohamed Abouahmed
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-03-10
Machine Learning In Microservices written by Mohamed Abouahmed 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 2023-03-10 with Computers categories.
Implement real-world machine learning in a microservices architecture as well as design, build, and deploy intelligent microservices systems using examples and case studies Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesDesign, build, and run microservices systems that utilize the full potential of machine learningDiscover the latest models and techniques for combining microservices and machine learning to create scalable systemsImplement machine learning in microservices architecture using open source applications with pros and consBook Description With the rising need for agile development and very short time-to-market system deployments, incorporating machine learning algorithms into decoupled fine-grained microservices systems provides the perfect technology mix for modern systems. Machine Learning in Microservices is your essential guide to staying ahead of the curve in this ever-evolving world of technology. The book starts by introducing you to the concept of machine learning microservices architecture (MSA) and comparing MSA with service-based and event-driven architectures, along with how to transition into MSA. Next, you'll learn about the different approaches to building MSA and find out how to overcome common practical challenges faced in MSA design. As you advance, you'll get to grips with machine learning (ML) concepts and see how they can help better design and run MSA systems. Finally, the book will take you through practical examples and open source applications that will help you build and run highly efficient, agile microservices systems. By the end of this microservices book, you'll have a clear idea of different models of microservices architecture and machine learning and be able to combine both technologies to deliver a flexible and highly scalable enterprise system. What you will learnRecognize the importance of MSA and ML and deploy both technologies in enterprise systemsExplore MSA enterprise systems and their general practical challengesDiscover how to design and develop microservices architectureUnderstand the different AI algorithms, types, and models and how they can be applied to MSAIdentify and overcome common MSA deployment challenges using AI and ML algorithmsExplore general open source and commercial tools commonly used in MSA enterprise systemsWho this book is for This book is for machine learning solution architects, system and machine learning developers, and system and solution integrators of private and public sector organizations. Basic knowledge of DevOps, system architecture, and artificial intelligence (AI) systems is assumed, and working knowledge of the Python programming language is highly desired.
Microservices For Machine Learning
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Author : Rohit Ranjan
language : en
Publisher: BPB Publications
Release Date : 2024-04-20
Microservices For Machine Learning written by Rohit Ranjan and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-20 with Computers categories.
Empowering AI innovations: The fusion of microservices and ML KEY FEATURES ● Microservices and ML fundamentals, advancements, and practical applications in various industries. ● Simplify complex ML development with distributed and scalable microservices architectures. ● Discover real-world scenarios illustrating the fusion of microservices and ML, showcasing AI's impact across industries. DESCRIPTION Explore the link between microservices and ML in Microservices for Machine Learning. Through this book, you will learn to build scalable systems by understanding modular software construction principles. You will also discover ML algorithms and tools like TensorFlow and PyTorch for developing advanced models. It equips you with the technical know-how to design, implement, and manage high-performance ML applications using microservices architecture. It establishes a foundation in microservices principles and core ML concepts before diving into practical aspects. You will learn how to design ML-specific microservices, implement them using frameworks like Flask, and containerize them with Docker for scalability. Data management strategies for ML are explored, including techniques for real-time data ingestion and data versioning. This book also addresses crucial aspects of securing ML microservices and using CI/CD practices to streamline development and deployment. Finally, you will discover real-world use cases showcasing how ML microservices are revolutionizing various industries, alongside a glimpse into the exciting future trends shaping this evolving field. Additionally, you will learn how to implement ML microservices with practical examples in Java and Python. This book merges software engineering and AI, guiding readers through modern development challenges. It is a guide for innovators, boosting efficiency and leading the way to a future of impactful technology solutions. WHAT YOU WILL LEARN ● Master the principles of microservices architecture for scalable software design. ● Deploy ML microservices using cloud platforms like AWS and Azure for scalability. ● Ensure ML microservices security with best practices in data encryption and access control. ● Utilize Docker and Kubernetes for efficient microservice containerization and orchestration. ● Implement CI/CD pipelines for automated, reliable ML model deployments. WHO THIS BOOK IS FOR This book is for data scientists, ML engineers, data engineers, DevOps team, and cloud engineers who are responsible for delivering real-time, accurate, and reliable ML models into production. TABLE OF CONTENTS 1. Introducing Microservices and Machine Learning 2. Foundation of Microservices 3. Fundamentals of Machine Learning 4. Designing Microservices for Machine Learning 5. Implementing Microservices for Machine Learning 6. Data Management in Machine Learning Microservices 7. Scaling and Load Balancing Machine Learning Microservices 8. Securing Machine Learning Microservices 9. Monitoring and Logging in Machine Learning Microservices 10. Deployment for Machine Learning Microservices 11. Real World Use Cases 12. Challenges and Future Trends
Machine Learning Theory And Applications
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Author : Xavier Vasques
language : en
Publisher: John Wiley & Sons
Release Date : 2024-01-31
Machine Learning Theory And Applications written by Xavier Vasques and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.
Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
Ai And Microservices
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Author : Dileep Kumar Pandiya
language : en
Publisher: Springer Nature
Release Date : 2025-07-01
Ai And Microservices written by Dileep Kumar Pandiya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Computers categories.
This book explores how artificial intelligence (AI) is transforming the design and operation of microservices and API architecture. It provides a clear and practical guide to using AI to automate tasks, enhance performance, and improve the scalability of microservice-based systems. Starting with the basics, you will learn about the core concepts of microservices and API design, gradually building an understanding of how AI can be seamlessly integrated. Through real-world examples, visual diagrams, and mock APIs, the book shows you how to bring theory into practice, making complex systems easier to manage and more efficient. You will also discover strategies for testing and scaling systems, securing APIs, and addressing ethical challenges in AI-powered environments. Case studies highlight successful implementations, offering valuable insights you can apply to your own projects. Whether you're a developer, architect, or tech enthusiast, this book gives you the tools and inspiration to build smarter, more resilient systems while staying ahead of future trends in AI and distributed computing. What You'll Learn: Understand the basics of microservices and API design and see how AI can make these systems smarter and more efficient. Discover how to use AI in microservices and APIs to automate tasks, improve performance, and boost security. Learn how to design scalable and secure systems by following best practices and innovative approaches. Get practical tips on troubleshooting and solving challenges in AI-powered microservice architectures. Who is this book for: Software architects and engineers, AI and machine learning professionals, and DevOps engineers
Design Innovation And Network Architecture For The Future Internet
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Author : Boucadair, Mohamed
language : en
Publisher: IGI Global
Release Date : 2021-04-16
Design Innovation And Network Architecture For The Future Internet written by Boucadair, Mohamed and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-16 with Computers categories.
For the past couple of years, network automation techniques that include software-defined networking (SDN) and dynamic resource allocation schemes have been the subject of a significant research and development effort. Likewise, network functions virtualization (NFV) and the foreseeable usage of a set of artificial intelligence techniques to facilitate the processing of customers’ requirements and the subsequent design, delivery, and operation of the corresponding services are very likely to dramatically distort the conception and the management of networking infrastructures. Some of these techniques are being specified within standards developing organizations while others remain perceived as a “buzz” without any concrete deployment plans disclosed by service providers. An in-depth understanding and analysis of these approaches should be conducted to help internet players in making appropriate design choices that would meet their requirements as well as their customers. This is an important area of research as these new developments and approaches will inevitably reshape the internet and the future of technology. Design Innovation and Network Architecture for the Future Internet sheds light on the foreseeable yet dramatic evolution of internet design principles and offers a comprehensive overview on the recent advances in networking techniques that are likely to shape the future internet. The chapters provide a rigorous in-depth analysis of the promises, pitfalls, and other challenges raised by these initiatives, while avoiding any speculation on their expected outcomes and technical benefits. This book covers essential topics such as content delivery networks, network functions virtualization, security, cloud computing, automation, and more. This book will be useful for network engineers, software designers, computer networking professionals, practitioners, researchers, academicians, and students looking for a comprehensive research book on the latest advancements in internet design principles and networking techniques.
Microservices And Serverless Architecture 2024 Edition Aws Azure Google
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2024-11-11
Microservices And Serverless Architecture 2024 Edition Aws Azure Google written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-11 with Business & Economics categories.
Welcome to "MICROSERVICES AND SERVERLESS ARCHITECTURE: Scalability and Efficiency in AWS, Azure, and Google Cloud - 2024 Edition", the definitive guide to transforming how you build applications in the modern era of cloud computing. Written by Diego Rodrigues, one of the most prolific authors, with over 180 titles published in six languages, this book offers a deep and practical journey to master the creation of distributed architectures, utilizing microservices and serverless to achieve new levels of scalability and efficiency. Whether you are a beginner or an experienced professional, this practical manual explores building solutions based on the three leading cloud platforms – AWS, Microsoft Azure, and Google Cloud. From setting up environments to implementing complex solutions with microservices, you will be guided step-by-step to create robust and highly scalable systems, ready for the future. You will learn to master both fundamental and advanced concepts, such as breaking down monolithic applications into independent microservices, using serverless architectures to eliminate the need for managing servers, and optimizing performance for the highest demands. The book also covers security practices, orchestration, and cloud service monitoring, ensuring that your solutions are not only efficient but also secure and resilient. Additionally, each chapter includes practical examples and case studies that challenge you to apply the knowledge acquired in real-world scenarios. This is the essential resource for developers, solution architects, and all those looking to stand out in the competitive IT market. Get ready to master the architectures shaping the future of modern applications and take your technological career to the next level with "MICROSERVICES AND SERVERLESS ARCHITECTURE." TAGS: Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques cybersecurity skills cybersecurity industry global cybersecurity trends Kali Linux tools cybersecurity education cybersecurity innovation penetration test tools cybersecurity best practices global cybersecurity companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle cybersecurity consulting cybersecurity framework network security cybersecurity courses cybersecurity tutorials Linux security cybersecurity challenges cybersecurity landscape cloud security cybersecurity threats cybersecurity compliance cybersecurity research cybersecurity technology
Hands On Microservices With C
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Author : Matt R. Cole
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-29
Hands On Microservices With C written by Matt R. Cole 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 2018-06-29 with Computers categories.
Build enterprise-grade microservice ecosystems with intensive case studies using C# Key Features Learn to build message-based microservices Packed with case studies to explain the intricacies of large-scale microservices Build scalable, modular, and robust architectures with C# Book Description C# is a powerful language when it comes to building applications and software architecture using rich libraries and tools such as .NET. This book will harness the strength of C# in developing microservices architectures and applications. This book shows developers how to develop an enterprise-grade, event-driven, asynchronous, message-based microservice framework using C#, .NET, and various open source tools. We will discuss how to send and receive messages, how to design many types of microservice that are truly usable in a corporate environment. We will also dissect each case and explain the code, best practices, pros and cons, and more. Through our journey, we will use many open source tools, and create file monitors, a machine learning microservice, a quantitative financial microservice that can handle bonds and credit default swaps, a deployment microservice to show you how to better manage your deployments, and memory, health status, and other microservices. By the end of this book, you will have a complete microservice ecosystem you can place into production or customize in no time. What you will learn Explore different open source tools within the context of designing microservices Learn to provide insulation to exception-prone function calls Build common messages used between microservices for communication Learn to create a microservice using our base class and interface Design a quantitative financial machine microservice Learn to design a microservice that is capable of using Blockchain technology Who this book is for C# developers, software architects, and professionals who want to master the art of designing the microservice architecture that is scalable based on environment. Developers should have a basic understanding of.NET application development using C# and Visual Studio
Revolutionizing Finance Leveraging Artificial Intelligence Machine Learning And Big Data For Smarter Credit Risk And Fraud Protection
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Author : Harish Kumar Sriram
language : en
Publisher: Deep Science Publishing
Release Date : 2025-04-26
Revolutionizing Finance Leveraging Artificial Intelligence Machine Learning And Big Data For Smarter Credit Risk And Fraud Protection written by Harish Kumar Sriram 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 2025-04-26 with Business & Economics categories.
In today’s fast-paced digital economy, financial institutions are facing increasing pressure to make smarter, faster, and more secure decisions. As global markets grow more interconnected and cyber threats more sophisticated, traditional approaches to credit risk assessment and fraud prevention are no longer sufficient. Revolutionizing Finance: Leveraging AI, ML, and Big Data for Smarter Credit Risk and Fraud Protection presents a forward-looking perspective on how intelligent technologies are transforming the foundations of financial security and trust. This book is the product of years of research, industry observation, and a deep belief that innovation is the key to sustainable financial health. Artificial intelligence (AI), machine learning (ML), and big data analytics have evolved from buzzwords into essential tools for financial resilience. They offer the ability to detect patterns, predict risk, and prevent fraud in ways that were unimaginable just a decade ago. Our goal is to demystify these technologies and demonstrate how they can be applied to create more dynamic and accurate credit models, reduce false positives in fraud detection, and increase operational efficiency. By blending theory with real-world applications, we provide readers with both the foundational knowledge and practical insights needed to embrace and implement these transformative tools. This book is designed for financial professionals, data scientists, policymakers, and anyone with a vested interest in the future of finance. We aim to empower readers with the confidence to lead change, harness data intelligently, and build systems that are not only reactive but predictive and proactive. As we stand at the intersection of finance and technology, we invite you to explore the possibilities and challenges that lie ahead. The journey to revolutionized finance starts here — and it's powered by intelligence, innovation, and data.
Building Microservices
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Author : Sam Newman
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2015-02-02
Building Microservices written by Sam Newman 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 2015-02-02 with Computers categories.
Annotation Over the past 10 years, distributed systems have become more fine-grained. From the large multi-million line long monolithic applications, we are now seeing the benefits of smaller self-contained services. Rather than heavy-weight, hard to change Service Oriented Architectures, we are now seeing systems consisting of collaborating microservices. Easier to change, deploy, and if required retire, organizations which are in the right position to take advantage of them are yielding significant benefits. This book takes an holistic view of the things you need to be cognizant of in order to pull this off. It covers just enough understanding of technology, architecture, operations and organization to show you how to move towards finer-grained systems.
Integrating Machine Learning Into Hpc Based Simulations And Analytics
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Author : Ben Youssef, Belgacem
language : en
Publisher: IGI Global
Release Date : 2024-12-13
Integrating Machine Learning Into Hpc Based Simulations And Analytics written by Ben Youssef, Belgacem and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-13 with Computers categories.
Researchers are increasingly using machine learning (ML) models to analyze data and simulate complex systems and phenomena. Small-scale computing systems used for training, validation, and testing of these ML models are no longer sufficient for grand-challenge problems characterized by large volumes of data generated at a much higher rate than before, surpassing by far the computing capabilities currently available in many cyberinfrastructure platforms. By associating high-performance computing (HPC) with ML environments, scientists and engineers would be able to enhance not only the scalability but also the performance of their predictive ML models. The Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics presents recent research efforts in designing and using ML techniques on HPC systems and discusses some of the results achieved thus far by cutting-edge relevant contributions. Covering topics such as data analytics, deep learning, and networking, this major reference work is ideal for computer scientists, academicians, engineers, researchers, scholars, practitioners, librarians, instructors, and students.