Book Image

Azure for Architects - Third Edition

By : Ritesh Modi, Jack Lee, Rithin Skaria
Book Image

Azure for Architects - Third Edition

By: Ritesh Modi, Jack Lee, Rithin Skaria

Overview of this book

Thanks to its support for high availability, scalability, security, performance, and disaster recovery, Azure has been widely adopted to create and deploy different types of application with ease. Updated for the latest developments, this third edition of Azure for Architects helps you get to grips with the core concepts of designing serverless architecture, including containers, Kubernetes deployments, and big data solutions. You'll learn how to architect solutions such as serverless functions, you'll discover deployment patterns for containers and Kubernetes, and you'll explore large-scale big data processing using Spark and Databricks. As you advance, you'll implement DevOps using Azure DevOps, work with intelligent solutions using Azure Cognitive Services, and integrate security, high availability, and scalability into each solution. Finally, you'll delve into Azure security concepts such as OAuth, OpenConnect, and managed identities. By the end of this book, you'll have gained the confidence to design intelligent Azure solutions based on containers and serverless functions.
Table of Contents (21 chapters)
20
Index

Azure AI processes

Every AI-based project is required to go through a certain set of steps before being operational. Let's explore these seven phases:

Data ingestion

In this phase, data is captured from various sources and stored such that it can be consumed in the next phase. The data is cleaned before being stored and any deviations from the norm are disregarded. This is part of the preparation of data. The data could have different velocity, variety, and volume. It can be structured similarly to relational databases, semi-structured like JSON documents, or unstructured like images, Word documents, and so on.

Data transformation

The data ingested is transformed into another format as it might not be consumable in its current format. The data transformation typically includes the cleaning and filtering of data, removing bias from the data, augmenting data by joining it with other datasets, creating additional data from existing data, and more. This is also part of...