Modern Data Architecture in AI

Building effective AI solutions demands a robust data architecture capable of handling vast, diverse, and real-time data. This book aims to provide a deep exploration of the tools, technologies, strategies, and best practices that necessitate the design, implementation, and management of data architectures tailored to AI.

The book starts by introducing fundamental concepts of modern data architecture for AI, laying the groundwork for understanding its importance. It then digs deep into the aspects of data ingestion and collection strategies. Subsequently, it discusses data storage and management techniques that cater specifically to AI workloads. Readers will understand the concepts of data processing, transformation, and building scalable and efficient data pipelines, and how to orchestrate interconnected processes. The book further explores the topics of scalable ML infrastructure and stream processing, concluding with insights into visualization, explainable AI, and future trends.

By the end of this book, the readers will have a comprehensive understanding and the skills to develop and manage scalable and efficient AI systems. They will have a firm grasp on the collection, storage, processing, and transformation of data, ensuring data governance and security. After reading this book, you will be well-equipped to design, build, and manage cutting-edge data architectures for diverse AI workloads, empowering your strategic initiatives.

WHAT YOU WILL LEARN
● Build data pipelines with automated orchestration and monitoring.
● Design scalable data lakes and lakehouse architectures for AI workloads.
● Learn data governance, security, and compliance frameworks.
● Leverage emerging technologies like quantum and edge computing.
● Optimize infrastructure for distributed ML training and serving.
● Visualize AI insights and apply explainable AI methods for transparency.
● Understand LLMs, generative AI, federated learning, and their data architecture impact.
● Architect real-time AI systems with online learning and low-latency stream processing.

WHO THIS BOOK IS FOR
This book is for data engineers, ML engineers, and enterprise architects who are at the forefront of designing and implementing scalable AI data systems. It is an essential guide for building robust data foundations. Software developers transitioning into AI infrastructure roles and technical leaders planning AI initiatives will also benefit significantly.

1. Introduction to Modern Data Architecture for AI
2. Data Collection and Ingestion Strategies
3. Data Storage and Management for AI Workloads
4. Data Processing and Transformation for AI
5. Modern Data Pipeline Management
6. Data Governance, Security, and Compliance in AI
7. AI Algorithms and Their Impact on Data Architecture
8. Scalable Machine Learning Infrastructure
9. Real-time AI Systems and Stream Processing
10. Data Visualization and Explainable AI
11. Emerging Trends in AI Data Architecture

● Abhik Choudhury, based in Exton, PA, USA, is an analytics managing consultant and data scientist with more than 13 years of experience in scalable data solutions. He specializes in AI/ML, cloud computing, database management, and big data technologies. Abhik excels in leading teams and collaborating with stakeholders to drive data-driven decisions in pharmacy, medical claims, and drug distribution. His technical skills include cloud solutions, business intelligence, data visualization, machine learning, and data warehousing. Proficient in Python, R, SQL, and various cloud data platforms like Databricks, Google Cloud, and AWS, he holds an MS in analytics from Georgia Institute of Technology. At IBM, Abhik designs data architecture solutions for healthcare and pharma clients, focusing on legal and compliance platforms. His previous roles include senior data scientist, lead business intelligence engineer, and business intelligence analyst at IBM, where he implemented data models, ETL pipelines, machine learning models, and analytical reports.

● Praneeth Reddy Amudala Puchakayala is an accomplished data scientist and designer of scalable, innovative solutions using machine learning, AI, and advanced analytics. He is experienced in several industry verticals, including financial services and healthcare, and he helps his clients achieve high impact with data-driven and results- oriented strategies. With a strong foundation in applied research and real-world problem-solving, Praneeth provides a thoughtful combination of technical depth along with practical implementation. He is an active contributor to the AI community as a speaker, reviewer, and mentor.

● Aishwarya Badlani is a passionate data scientist and analytics leader specializing in transforming complex data into strategic business insights. With a background spanning marketing analytics, customer behavior modeling, and AI-powered decision systems, she brings a unique blend of technical expertise and business acumen to every project.Aishwarya has made significant contributions across the retail and e-commerce industries, where she has helped drive customer-centric growth through data innovation. Her work integrates advanced analytics, experimentation, and AI to solve real-world challenges at scale. An advocate for continuous learning and mentorship, Aishwarya actively engages with the data and AI community as a speaker, collaborator, and lifelong learner.