Celery Containers and Asynchronous ML Microservices : Production Infrastructure Architecture

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This technical report establishes the architectural rationale, task worker mechanics, generic industry taxonomies, technology stack integration, and enterprise deployment strategies for operating containerized Celery worker pools alongside distributed LLM inference clusters. 1. Architectural Rationale: Why Celery Containers Are Used Containerized Celery workers serve as the primary asynchronous execution layer in distributed ML architectures. Web API pods and background Celery workers exhibit vastly different scaling profiles.

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