Scalability, Latency, Flexibility: The Case for Similarity Search as a Service
Amir Sadoughi
2021 Contributed Talk 10
in
Workshop: Personalized Recommendation Systems and Algorithms
in
Workshop: Personalized Recommendation Systems and Algorithms
Abstract
Modern deep learning models can represent arbitrary objects as vectors, also known as embeddings. Software applications can use these deep learning models and their respective embeddings to power a variety of use cases, including personalization, recommendation systems, image search, anomaly detection, and more. To date, software engineers could build these systems by integrating open source k-nearest neighbor libraries with an off-the-shelf web server. However, using such a solution presents serious challenges in the face of scalability, latency, and flexibility. To address these challenges, we built Pinecone, providing similarity search as a service.
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