Building Data Pipelines for AI Applications: A Practical Guide Engineering, Feature and Enabled Chatbots

The modern landscape of Artificial Intelligence has reached a critical inflection point where the sheer complexity of algorithms is no longer the sole determinant of success, rather, the intelligence of a system is now fundamentally tethered to the sophistication, reliability, and scalability of its underlying data engineering infrastructure. As industries ranging from healthcare to cybersecurity attempt to move beyond experimental models into robust production environments, they are discovering that the true bottleneck lies in the construction of seamless data pipelines that can ingest, transform, and govern massive streams of information in real time. This book serves as a comprehensive bridge between the high level theoretical research of AI and the gritty, high stakes reality of large scale systems engineering, arguing that the data pipeline is the literal backbone of the intelligent era. It shifts the focus from a model centric view to a data centric one, where the ability to build scalable data architectures, manage intricate feature engineering for machine learning, and maintain high-speed retrieval systems like vector databases becomes the primary competitive advantage.

Pagina's: 260, Paperback, LAP LAMBERT Academic Publishing