Data Engineering for AI/ML Pipelines

Data engineering is the art of building and managing data pipelines that enable efficient data flow for AI/ML projects. This book serves as a comprehensive guide to data engineering for AI/ML systems, equipping you with the knowledge and skills to create robust and scalable data infrastructure.

This book covers everything from foundational concepts to advanced techniques. It begins by introducing the role of data engineering in AI/ML, followed by exploring the lifecycle of data, from data generation and collection to storage and management. Readers will learn how to design robust data pipelines, transform data, and deploy AI/ML models effectively for real-world applications. The book also explains security, privacy, and compliance, ensuring responsible data management. Finally, it explores future trends, including automation, real-time data processing, and advanced architectures, providing a forward-looking perspective on the evolution of data engineering.

By the end of this book, you will have a deep understanding of the principles and practices of data engineering for AI/ML. You will be able to design and implement efficient data pipelines, select appropriate technologies, ensure data quality and security, and leverage data for building successful AI/ML models.

KEY FEATURES

* Comprehensive guide to building scalable AI/ML data engineering pipelines.
* Practical insights into data collection, storage, processing, and analysis.
* Emphasis on data security, privacy, and emerging trends in AI/ML.

WHAT YOU WILL LEARN

* Architect scalable data solutions for AI/ML-driven applications.
* Design and implement efficient data pipelines for machine learning.
* Ensure data security and privacy in AI/ML systems.
* Leverage emerging technologies in data engineering for AI/ML.
* Optimize data transformation processes for enhanced model performance.

WHO THIS BOOK IS FOR

This book is ideal for software engineers, ML practitioners, IT professionals, and students wanting to master data pipelines for AI/ML. It is also valuable for developers and system architects aiming to expand their knowledge of data-driven technologies.

1. Introduction to Data Engineering for AI/ML
2. Lifecycle of AI/ML Data Engineering
3. Architecting Data Solutions for AI/ML
4. Technology Selection in AI/ML Data Engineering
5. Data Generation and Collection for AI/ML
6. Data Storage and Management in AI/ML
7. Data Ingestion and Preparation for ML
8. Transforming and Processing Data for AI/ML
9. Model Deployment and Data Serving
10. Security and Privacy in AI/ML Data Engineering
11. Emerging Trends and Future Direction

Venkata Karthik Penikalapati is a seasoned software developer with over a decade of expertise in designing and managing intricate distributed systems, data pipelines, and ML Ops. Karthik holds a master’s degree in computer science from the University at Buffalo. His knowledge spans the realms of machine learning, data engineering, and workflow orchestration. Currently, Karthik is a valuable member of the Salesforce team within the Search Cloud division. Here, he’s at the forefront of cutting-edge developments, spearheading the integration of the latest advancements in Artificial Intelligence (AI).

Mitesh Mangaonkar, an experienced data engineering professional specializing in artificial intelligence and machine learning. With over a decade of experience in data engineering, he has played pivotal roles in designing and implementing sophisticated systems for fraud detection and risk mitigation, particularly within Airbnb’s Trust and Safety team. He holds a Master of Science in Management Information Systems from Texas Tech University and a Bachelor of Engineering in Information Technology from the University of Mumbai. His expertise spans big data, data architecture, and cloud computing. He has a notable history of contributions to Airbnb and Amazon Web Services, where he guided Fortune 500 firms through major cloud data migrations. He had the opportunity to share his insights at numerous international conferences and has published several influential papers in top-tier IEEE, Springer, and ACM journals.