Building Intelligent Applications With Luis Ai
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Building Intelligent Applications with Luis.ai
"Building Intelligent Applications with Luis.ai" Unlock the full potential of conversational AI with "Building Intelligent Applications with Luis.ai," a comprehensive guide designed for professionals, architects, and developers seeking to master intelligent application development using Microsoft’s powerful Luis.ai platform. Beginning with a thorough overview of conversational intelligence, the book offers a deep exploration of Luis.ai’s architecture, foundational concepts, and its strategic integration within Azure Cognitive Services. Practical insights on languages, frameworks, and application patterns are balanced with guidance on ethical, security, and privacy considerations—equipping readers to design solutions that are both innovative and responsible. Delving into the intricacies of language understanding, the book methodically covers the design, modeling, and management of Luis.ai solutions, addressing topics such as intent taxonomy, entity extraction, data annotation, and advanced model composition. Readers are guided through robust training, testing, and validation methodologies, with a focus on iterative improvement, bias detection, and performance testing in real-world scenarios. Chapters devoted to API integration, scalable deployments, and real-time architectures ensure practical, production-ready implementations across diverse platforms and modalities. Beyond development fundamentals, the book addresses the operational realities of intelligent applications, with best practices for telemetry, monitoring, analytics, DevOps automation, and compliance. Security, privacy, and regulatory requirements are examined in depth, emphasizing auditability and incident response. The final sections explore cutting-edge trends, such as integrating large language models, deploying at the intelligent edge, and advancing personalization—all culminating in a forward-looking perspective on the challenges and opportunities shaping the next era of intelligent conversational agents.
Building Intelligent Apps with .NET and Azure AI Services
Author: Ashirwad Satapathi
language: en
Publisher: Springer Nature
Release Date: 2024-08-31
This comprehensive book equips you with the knowledge and skills you need to develop intelligent solutions by leveraging the capabilities of Azure AI Services. The book adopts a practical and example-based approach to help you easily grasp the subject matter and apply it effectively. The book begins by covering essential topics that lay the foundation for understanding Azure AI Services. You will know how to provision Azure AI Services and seamlessly integrate them into your .NET applications. Through detailed and illustrative examples, you will be guided step-by-step in building intelligent solutions that target a range of platforms. In addition to the core concepts, the book delves into various AI services, offering practical guidance on building solutions for a wide array of scenarios. From text translation to image classification, you will learn how to harness the power of Azure AI Services to solve complex problems. The book presents near-production scenarios and includes carefully crafted lab exercises to provide you with a hands-on learning experience. What You Will Learn Understand various Azure AI services required to build intelligent apps Perform language-based document classification with Azure AI Language Service Build a multi-language text translator app with .NET MAUI Integrate GenAI capabilities to your applications by leveraging Azure OpenAI Who This Book Is For Developers, cloud architects, and tech enthusiasts looking forward to building scalable and intelligent solutions using Azure AI Services and .NET
Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud
Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud" is a comprehensive guide for data scientists, machine learning engineers, and developers seeking to leverage the power of cloud computing for building, training, deploying, and managing AI models at scale. The book begins by establishing a strong foundation in cloud computing principles and core machine learning concepts, including supervised, unsupervised, and reinforcement learning, as well as neural network architectures. The core of the book dives into the AI/ML offerings of the three major cloud providers: AWS, Azure, and Google Cloud. For AWS, the book explores Amazon SageMaker in detail, covering model building, training, hyperparameter tuning, and deployment strategies like real-time inference and batch transform. It also examines AWS AI services like Rekognition, Comprehend, Translate, and Polly. The Azure section focuses on Azure Machine Learning, including workspaces, automated ML, the Designer interface, and MLOps integration with Azure DevOps. It also covers Azure Cognitive Services, exploring Vision, Speech, Language, and Decision APIs. The Google Cloud section delves into Vertex AI, covering Workbench, custom training, pre-trained models, and MLOps with Vertex AI Pipelines. It also explores Google Cloud AI APIs like the Vision, Natural Language, and Translation APIs, along with Dialogflow for conversational AI. The book then transitions to advanced topics and best practices, covering scaling AI deployments with containerization (Docker and Kubernetes) and serverless computing. It also addresses crucial aspects like monitoring, logging, debugging, security, compliance (GDPR, HIPAA, PCI DSS), and cost optimization strategies. The conclusion looks at the future of cloud AI, discussing emerging trends like edge AI, TinyML, explainable AI, generative AI, and responsible AI practices. Throughout the book, practical examples, code snippets, and comparative analyses are provided to enhance understanding and facilitate real-world application of the concepts. This makes "Cloud AI Mastery" a practical resource for anyone looking to master cloud-based AI deployments.