Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions Purchase of the print or Kindle book includes a free PDF eBook Key Features Translate and implement conceptual architectures with the right Azure services Inject artificial intelligence into data solutions for advanced analytics Leverage cloud computing and frameworks to drive data science workloads Book DescriptionWith data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions. By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.What you will learn Design scalable and cost-effective cloud data platforms on Microsoft Azure Explore architectural design patterns with various use cases Determine the right data stores and data warehouse solutions Discover best practices for data orchestration and transformation Help end users to visualize data using interactive dashboarding Leverage OpenAI and custom ML models for advanced analytics Manage security, compliance, and governance for the data estate Who this book is forThis book is for anyone looking to elevate their skill set to the level of an architect. Data engineers, data scientists, business intelligence developers, and database administrators who want to learn how to design end-to-end data solutions and get a bird’s-eye view of the entire data platform will find this book useful. Although not required, basic knowledge of databases and data engineering workloads is recommended.

Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect's perspective to developing end-to-end solutions Purchase of the print or Kindle book includes a free PDF eBook Key Features: Translate and implement conceptual architectures with the right Azure services Inject artificial intelligence into data solutions for advanced analytics Leverage cloud computing and frameworks to drive data science workloads Book Description: With data's growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You'll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you'll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you'll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you'll discover how to add data governance, compliance, and security to solutions. By the end of this book, you'll have gained the expertise needed to become a well-rounded Azure Data & AI architect. What You Will Learn: Design scalable and cost-effective cloud data platforms on Microsoft Azure Explore architectural design patterns with various use cases Determine the right data stores and data warehouse solutions Discover best practices for data orchestration and transformation Help end users to visualize data using interactive dashboarding Leverage OpenAI and custom ML models for advanced analytics Manage security, compliance, and governance for the data estate Who this book is for: This book is for anyone looking to elevate their skill set to the level of an architect. Data engineers, data scientists, business intelligence developers, and database administrators who want to learn how to design end-to-end data solutions and get a bird's-eye view of the entire data platform will find this book useful. Although not required, basic knowledge of databases and data engineering workloads is recommended.

A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial Intelligence Key Features Learn to build and run a big data application with sample code Explore examples to implement activities that a big data architect performs Use Machine Learning and AI for structured and unstructured data Book Description The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action. What you will learn Learn Hadoop Ecosystem and Apache projects Understand, compare NoSQL database and essential software architecture Cloud infrastructure design considerations for big data Explore application scenario of big data tools for daily activities Learn to analyze and visualize results to uncover valuable insights Build and run a big data application with sample code from end to end Apply Machine Learning and AI to perform big data intelligence Practice the daily activities performed by big data architects Who this book is for Big Data Architect’s Handbook is for you if you are an aspiring data professional, developer, or IT enthusiast who aims to be an all-round architect in big data. This book is your one-stop solution to enhance your knowledge and carry out easy to complex activities required to become a big data architect.

Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key Features Discover Azure AI services, including computer vision, Auto ML, NLP, and OpenAI Explore AI use cases, such as image identification, chatbots, and more Work through 145 practice questions under chapter-end self-assessments and mock exams Purchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tips Book Description The AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud. This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You'll explore Microsoft's responsible AI principles like safety and accountability. Then, you'll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you'll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you'll find chapter review questions with answers, provided as an online resource. By the end of this exam guide, you'll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources. What you will learn Discover various types of artificial intelligence (AI)workloads and services in Azure Cover Microsoft's guiding principles for responsible AI development and use Understand the fundamental principles of how AI and machine learning work Explore how AI models can recognize content in images and documents Gain insights into the features and use cases for natural language processing Explore the capabilities of generative AI services Who this book is for Whether you're a cloud engineer, software developer, an aspiring data scientist, or simply interested in learning AI/ML concepts and capabilities on Azure, this book is for you. The book also serves as a foundation for those looking to attempt more advanced AI and data science-related certification exams (e.g. Microsoft Certified: Azure AI Engineer Associate). Although no experience in data science and software engineering is required, basic knowledge of cloud concepts and client-server applications is assumed.

How can you make sense of the complex IoT landscape? With dozens of components ranging from devices to metadata about the devices, it's easy to get lost among the possibilities. But it's not impossible if you have the right guide to help you navigate all the complexities. This practical book shows developers, architects, and IT managers how to build IoT solutions on Azure. Author Blaize Stewart presents a comprehensive view of the IoT landscape. You'll learn about devices, device management at scale, and the tools Azure provides for building globally distributed systems. You'll also explore ways to organize data by choosing the appropriate dataflow and data storage technologies. The final chapters examine data consumption and solutions for delivering data to consumers with Azure. Get the architectural guidance you need to create holistic solutions with devices, data, and everything in between. This book helps you: Meet the demands of an IoT solution with Azure-provided functionality Use Azure to create complete scalable and secure IoT systems Understand how to articulate IoT architecture and solutions Guide conversations around common problems that IoT applications solve Select the appropriate technologies in the Azure space to build IoT applications

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language Key FeaturesCreate, deploy, productionalize, and scale automated machine learning solutions on Microsoft AzureImprove the accuracy of your ML models through automatic data featurization and model trainingIncrease productivity in your organization by using artificial intelligence to solve common problemsBook Description Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect. What you will learnUnderstand how to train classification, regression, and forecasting ML algorithms with Azure AutoMLPrepare data for Azure AutoML to ensure smooth model training and deploymentAdjust AutoML configuration settings to make your models as accurate as possibleDetermine when to use a batch-scoring solution versus a real-time scoring solutionProductionalize your AutoML and discover how to quickly deliver valueCreate real-time scoring solutions with AutoML and Azure Kubernetes ServiceTrain a large number of AutoML models at once using the AzureML Python SDKWho this book is for Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book.

Overcome data mesh adoption challenges using the cloud-scale analytics framework and make your data analytics landscape agile and efficient by using standard architecture patterns for diverse analytical workloads Key Features Delve into core data mesh concepts and apply them to real-world situations Safely reassess and redesign your framework for seamless data mesh integration Conquer practical challenges, from domain organization to building data contracts Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDecentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help. The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you’ll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud. The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI). By the end of this book, you’ll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.What you will learn Build a strategy to implement a data mesh in Azure Cloud Plan your data mesh journey to build a collaborative analytics platform Address challenges in designing, building, and managing data contracts Get to grips with monitoring and governing a data mesh Understand how to build a self-service portal for analytics Design and implement a secure data mesh architecture Resolve practical challenges related to data mesh adoption Who this book is for This book is for chief data officers and data architects of large and medium-size organizations who are struggling to maintain silos of data and analytics projects. Data architects and data engineers looking to understand data mesh and how it can help their organizations democratize data and analytics will also benefit from this book. Prior knowledge of managing centralized analytical systems, as well as experience with building data lakes, data warehouses, data pipelines, data integrations, and transformations is needed to get the most out of this book.