ADAPTIVE FEATURE ENGINEERING PIPELINES FOR ENTERPRISE AI SYSTEMS USING REAL-TIME LAKEHOUSE DATA PROCESSING AND DATAOPS AUTOMATION

Abstract

Various transactional applications, IoT nodes, customer engagement, and operational platforms generate large volumes of heterogeneous data that run enterprise AI systems. But the traditional feature engineering methods rely on pre-defined pipelines, manual transformations, and periodic batch processing, making it challenging for AI models to adapt to new data patterns as they emerge. The paper outlines an adaptive feature-engineering framework that leverages the real-time data-processing capabilities of a lakehouse and DataOps automation to scale and continually evolve enterprise AI systems. The proposed model, which integrates all streaming and batch data into a single lakehouse, provides automatic feature extraction, transformation, validation, and deployment throughout the entire AI lifecycle. This approach integrates DataOps practices for automated data quality monitoring, pipeline orchestration, versioning, and drift detection to enhance reliability and streamline operations. Besides, feature pipelines come with adaptive capabilities that enable them to adjust to data changes and evolving business needs while preserving governance and traceability. The objective of the framework is to reduce feature-engineering latency, improve model performance, and enhance scalability in enterprise settings. The study offers a conceptual blueprint and a pathway to implement real-time, data-driven AI solutions with smart, automated feature management tools for organizations aspiring to operationalize artificial intelligence in real time.