Textdokument
Blueprint for a Production-Ready Information Retrieval System based on Multi-Modal Embeddings
Vorschaubild nicht verfügbar
Volltext URI
Dokumententyp
Dateien
Datum
2021
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Quelle
INFORMATIK 2021
Workshop: Künstliche Intelligenz für kleine und mittlere Unternehmen (KI-KMU 2021)
Verlag
Gesellschaft für Informatik, Bonn
Zusammenfassung
Deep Learning models for mapping documents from different domains, e.g., text, images, and audio, into a common vector space, enable a seamless information retrieval between the different domains and, thus, significantly improve the user experience of many expert tools. Despite various models for multi-modal mappings presented in scientific literature, the implementation and integration remain a challenge within the industry, especially for small or medium-sized companies. Reasons are, that developing such retrieval systems for production use-cases is a non-trivial task, requiring scalable, reliable, and cost-efficient infrastructure, services as well as adequate Deep Learning models. We present a generic and flexible blueprint architecture, targeting the development of a production-ready image-text retrieval search system using Kubernetes, MLflow, Elasticsearch, and integrating state-of-the-art Deep Learning models.