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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.
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
This book constitutes the refereed proceedings of the 15th International Conference on Software Architecture, ECSA 2021, held in Sweden, in September 2021. Due to COVID-19 pandemic the conference was held virtually. In the Research Track, 11 full papers presented together with 5 short papers were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections as follows: architectures for reconfigurable and self-adaptive systems; machine learning for software architecture; architectural knowledge, decisions, and rationale; architecting for quality attributes ̧ hitecture-centric source code analysis
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
Summary Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of forecasting tedious business processes and back-office tasks Envision quickly gauging customer sentiment from social media content (even large volumes of it). Consider the competitive advantage of making decisions when you know the most likely future events Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning can deliver huge benefits for everyday business tasks. With some guidance, you can get those big wins yourself without complex math or highly paid consultants! If you can crunch numbers in Excel, you can use modern ML services to efficiently direct marketing dollars, identify and keep your best customers, and optimize back office processes. This book shows you how. About the book Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you’ll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you’ll use the Amazon SageMaker ML service, which makes it a snap to turn your questions into results. What's inside Identifying tasks suited to machine learning Automating back office processes Using open source and cloud-based tools Relevant case studies About the reader For technically inclined business professionals or business application developers. About the author Doug Hudgeon and Richard Nichol specialize in maximizing the value of business data through AI and machine learning for companies of any size. Table of Contents: PART 1 MACHINE LEARNING FOR BUSINESS 1 ¦ How machine learning applies to your business PART 2 SIX SCENARIOS: MACHINE LEARNING FOR BUSINESS 2 ¦ Should you send a purchase order to a technical approver? 3 ¦ Should you call a customer because they are at risk of churning? 4 ¦ Should an incident be escalated to your support team? 5 ¦ Should you question an invoice sent by a supplier? 6 ¦ Forecasting your company’s monthly power usage 7 ¦ Improving your company’s monthly power usage forecast PART 3 MOVING MACHINE LEARNING INTO PRODUCTION 8 ¦ Serving predictions over the web 9 ¦ Case studies
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.
Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.
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.
Artificial Intelligence for Autonomous Networks introduces the autonomous network by juxtaposing two unique technologies and communities: Networking and AI. The book reviews the technologies behind AI and software-defined network/network function virtualization, highlighting the exciting opportunities to integrate those two worlds. Outlining the new frontiers for autonomous networks, this book highlights their impact and benefits to consumers and enterprise customers. It also explores the potential of the autonomous network for transforming network operation, cyber security, enterprise services, 5G and IoT, infrastructure monitoring and traffic optimization, and finally, customer experience and care. With contributions from leading experts, this book will provide an invaluable resource for network engineers, software engineers, artificial intelligence, and machine learning researchers.
Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming Key Features • Work on streaming use cases that are not taught in most data science courses • Gain experience with state-of-the-art tools for streaming data • Mitigate various challenges while handling streaming data Book Description Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models. What you will learn • Understand the challenges and advantages of working with streaming data • Develop real-time insights from streaming data • Understand the implementation of streaming data with various use cases to boost your knowledge • Develop a PCA alternative that can work on real-time data • Explore best practices for handling streaming data that you absolutely need to remember • Develop an API for real-time machine learning inference Who this book is for This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.