Real-Time ML and LLM Optimization: Orchestrating Scalable Workflows in Distributed Commerce Environments

Due to the exponential increase of the data in the distributed commerce settings, new issues of the real-time decision making and operational scale have emerged. In order to solve these problems, this paper proposes an orchestration framework that brings together real-time machine learning (ML) models with large language models (LLMs) to optimize distributed workflows in e-commerce ecosystems. The suggested framework relies on micro services-based architecture, edgecloud synergy, and semantic data pipelines to enable low-latency inference, adaptive learning, and context-aware automation. With the integration of reinforcement learning to adapt dynamic workflows and intelligent task coordination and personalized customer interactions based on LLMs, the system provides efficiency in operations and enhanced user interaction. Evaluations on a real-world retail data set show, under varying load conditions, large latency reduction and throughput gains as well as high model accuracy. The scalability, fault tolerance and DataOps/MLOps pipeline integration are also studied, which makes the model applicable to enterprise-level deployment across multi-cloud setups.