From Batch to Streaming: Building Real-time Inference Pipelines for Machine Learning

Abstract

Modern machine learning applications are experiencing a fundamental shift from traditional batch processing toward real-time inference pipelines, driven by the increasing demand for timely and context-aware predictions. This article comprehensively explores different training and serving architectures, ranging from conventional batch processing to sophisticated streaming approaches. It examines the evolution of ML pipelines, discussing the advantages and challenges of various architectural patterns, including batch training with batch predictions, batch training with streaming predictions, and fully streaming approaches. The article delves into the implementation considerations for each architecture, addressing critical challenges such as data freshness, concept drift, and model degradation. It also explores continual learning systems, representing the cutting edge of adaptive ML architectures. The article includes a detailed analysis of best practices for implementation, covering architecture selection, system design considerations, and operational excellence. Through this systematic examination, the article provides practitioners with a structured framework for selecting and implementing appropriate ML pipeline architectures based on their specific requirements and constraints.