Название: Data Engineering for Multimodal AI: Architecting Scalable Systems for Next-Generation AI Applications (Final Release)
Автор: Vasundra Srinivasan
Издательство: O’Reilly Media, Inc.
Год: 2026
Страниц: 635
Язык: английский
Формат: epub
Размер: 23.6 MB

AI is only as capable as the context it’s given, and context is a data engineering problem before it’s a model problem. As multimodal AI systems and applications become increasingly sophisticated and data-hungry, the infrastructure that produces and governs that context has to evolve to keep pace.

Data Engineering for Multimodal AI is one of the first practical guides for data engineers, Machine Learning engineers, and MLOps specialists looking to rapidly master the skills needed to build robust, scalable data infrastructures that multimodal AI systems and applications depend on for effective context engineering. You'll follow the entire lifecycle of AI-driven data engineering, from conceptualizing data architectures to implementing data pipelines optimized for multimodal learning in both cloud-native and on-premises environments. And each chapter includes step-by-step guides and best practices for implementing key concepts. Examples in Python.

Design and implement cloud-native data architectures optimized for multimodal AI workloads
Build efficient and scalable ETL processes for preparing diverse AI training data
Implement real-time data processing pipelines for multimodal AI inference
Develop and manage feature stores that support multiple data modalities
Apply data governance and security practices specific to multimodal AI projects
Optimize data storage and retrieval for various types of multimodal ML models
Integrate data versioning and lineage tracking in multimodal AI workflows
Implement data-quality frameworks to ensure reliable outcomes across data types
Design data pipelines that support responsible AI practices in a multimodal context

Who Should Read This Book:
This book is for practitioners building AI systems that reason across more than one kind of data. If you are a data engineer, a Machine Learning engineer, a platform engineer, a tech lead, or an architect, you will find yourself throughout these pages. If you are an applied scientist or a software engineer moving into AI infrastructure, the chapters on features, retrieval, and orchestration will give you the mental models your teammates are already assuming you have.

You will get the most out of this book if you are comfortable with cloud-native concepts, have touched at least one streaming platform or batch framework, and have a rough sense of what an embedding is and why a vector index exists. You do not need to understand a transformer from the inside. You do not need a Machine Learning research background. What you do need is a willingness to think in systems, to care about the relationships between modalities, and to treat data quality as a design problem rather than a checklist. If you are early in your career, this book will stretch you, and that is by design. The playbooks will give you working multimodal builds that very few engineers at your level have ever shipped.

If you are a senior engineer or a tech lead, the architectural patterns and cross-industry framing are meant to save you months of discovery on your next multimodal initiative. If you are a manager or an architect who sets the direction for initiatives, this book will give you a shared vocabulary you can hand to your team.