About This Architecture

Classifier Distributor Storage Architecture orchestrates ML model outputs and vector embeddings across PostgreSQL with pgvector and MinIO object storage. The Classifier Distributor routes processed data to PostgreSQL for structured job offer data, normalized attributes, and professional classifications, while simultaneously persisting embeddings vectors, processing metadata, logs, and intermediate artifacts to MinIO. This dual-storage pattern separates relational structured data from unstructured artifacts, enabling efficient vector similarity search and audit trails. Fork this diagram on Diagrams.so to customize storage backends, add caching layers, or integrate additional classifiers. The architecture demonstrates best practices for production ML systems requiring both transactional consistency and scalable object storage.

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How should I architect storage for an ML classification system that needs both vector embeddings and artifact persistence?

This diagram shows a Classifier Distributor that routes processed data to PostgreSQL with pgvector for structured job offers, normalized attributes, and professional classifications, while MinIO stores embeddings vectors, processing metadata, logs, and intermediate artifacts. This separation enables efficient vector similarity search in PostgreSQL while maintaining scalable object storage for audi