Description
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.

Table of Contents
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

 

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Spis treści

Data Engineering for AI/ML Pipelines eBook -- spis treści