Böcker av Abhishek Mishra

Understand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation.This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies.The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements.What You Will LearnDevelop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processingKnow the significance of design patterns in creating robust intelligent solutionsUnderstand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systemsBe aware of the ethical implications of scalable AI systemsWho This Book Is ForAI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience

Learn Azure-based features to build and deploy Java applications on Microsoft's Azure cloud platform. This book provides examples of components on Azure that are of special interest to Java programmers, including the different deployment models that are available. The book shows how to deploy your Java applications in Azure WebApp, Azure Kubernetes Service, Azure Functions, and Azure Spring Cloud. Also covered is integration with components such as Graph API, Azure Storage, Azure Redis Cache, and Azure SQL. The book begins with a brief discussion of cloud computing and an introduction to Java support on Azure. You'll then learn how to deploy Java applications using each of the deployment models, and you'll see examples of integrating with Azure services that are of particular interest to Java programmers. Security is an important aspect, and this book shows you how to enable authentication and authorization for your Java applications using Azure Active Directory. Implementing a DevOps strategy is essential in today's market when building any application. Examples in this book show you how to build continuous integration and continuous deployment pipelines to build and deploy Java applications on Azure. The book focuses on the best practices you should follow while designing and implementing Java applications on Azure. The book also elaborates on monitoring and debugging Java applications running on Azure using Application Insights and Azure Monitor.  What You Will LearnDesign and build Azure-based Java applicationsRun Azure-based Java applications on services such as Azure App Services, Azure Spring Cloud, Azure Functions, and Azure Kubernetes ServiceIntegrate Azure services such as Azure SQL, Azure Storage Account, Azure Redis Cache, Azure Active Directory, and more with Java applications running on Azure Monitor and debug Java applications running on AzureSecure Azure-based Java applicationsBuild DevOps CI/CD strategy for Azure-based Java applicationsPackage and deploy Azure-based Java applications on Azure Who This Book Is ForJava developers planning to build Azure-based Java applications and deploy them on Azure. Developers should be aware of the preliminary cloud fundamentals to help them understand the Java capability available on Azure. They do not need to be an expert in Azure to grasp the book's content and start building Java-based applications using the capability available on Azure. However, they should have a good understanding of the Java programming language and frameworks.  

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Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner!Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications.Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models--both pre-trained and user-built--with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers:* Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics* Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming* Develop skills in data acquisition and modeling, classification, and regression.* Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS)* Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreMLMachine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.

Put the power of AWS Cloud machine learning services to work in your business and commercial applications!Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services.Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems.* Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building* Discover common neural network frameworks with Amazon SageMaker* Solve computer vision problems with Amazon Rekognition* Benefit from illustrations, source code examples, and sidebars in each chapterThe book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.

Jump into the app development world with confidence! iOS Swift 24-Hour Trainer combines book and video lessons in Apple's Swift programming language to prepare you to build iPhone and iPad apps and distribute them through the Appstore.