Book Image

Modern Data Architecture on AWS

By : Behram Irani
5 (1)
Book Image

Modern Data Architecture on AWS

5 (1)
By: Behram Irani

Overview of this book

Many IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.
Table of Contents (24 chapters)
1
Part 1: Foundational Data Lake
5
Part 2: Purpose-Built Services And Unified Data Access
17
Part 3: Govern, Scale, Optimize And Operationalize

Role of AI/ML in predictive analytics

Before we get into the role of AI/ML, let’s quickly understand how AI, ML, and deep learning (DL) are co-related.

AI refers to a field of computer science that focuses on creating intelligent machines or systems that can perform tasks that typically require human intelligence. AI aims to simulate human cognitive processes such as learning, reasoning, problem-solving, perception, and language understanding. Out of the many possibilities, some examples of AI are speech recognition, computer vision (CV), natural language processing (NLP), learning, and problem-solving.

ML is a subfield of AI that focuses on the development of algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed. ML also gets referred to as predictive analytics since it’s able to predict outcomes. Examples of ML usage in business terms would be sales forecasting, fraud detection, sentiment analysis...