Conceptual Approaches to Organizing Feature Stores in High-Load Machine Learning Systems

Abstract

The paper examines conceptual approaches to organizing feature stores in high-load machine learning systems. The growth of production ML has exposed persistent problems, including duplicated feature logic, training–serving skew, and brittle pipelines, particularly under low-latency inference and heavy batch training workloads. This paper proposes an original analytical framework for organizing feature stores under high-load conditions. The framework links internal architectural decisions (registry design, transformation semantics, offline/online materialization, synchronization, geo-distribution, and maintenance control) to operational objectives (latency, point-in-time correctness, reproducibility, governance, and cost discipline), enabling structured design selection rather than a descriptive survey of existing systems. The study characterizes functional blocks of a feature store (feature registry, transformation layer, offline and online materializations, and serving interfaces), then analyzes consistency guarantees, point-in-time correctness, refresh strategies, and geo-distribution patterns. Methods rely on a comparative analysis of recent systems and surveys, with a focus on scalability bottlenecks in joins, cache design, and maintenance scheduling. The conclusion formulates design recommendations for selecting storage layouts, synchronization policies, and operational controls across the ML lifecycle. The paper targets engineers and researchers working on production MLOps and data infrastructure. Implications for feature reuse and compliance are discussed.