Conference:  OpenAI + Data Forum 2022

Authors: Charles Xie, Frank Liu

2022-06-22

The total amount of digital data generated worldwide is increasing at a rapid rate. Simultaneously, approximately 80% (and growing) of this newly generated data is unstructured data - data that does not conform to a table- or object-based model. Examples of unstructured data include text, images, protein structures, geospatial information, and IoT data streams. Despite this, the vast majority of companies and organizations do not have a way of storing and analyzing these increasingly large quantities of unstructured data. Embeddings - high-dimensional, dense vectors which represent the semantic content of unstructured data - can remedy this. Armed with this knowledge, it's clear that the mobile/IoT era necessitates a new type of cloud-native, fully distributed database purpose-built to store, search, and index large quantities of embedding vectors: Milvus.In this presentation, we'll introduce the design of Milvus 2.0 - the world's most popular open-source vector database trusted by over 1000 organizations. Milvus 2.0 represents a complete paradigm shift in the underlying vector database architecture - cloud-native, horizontally scalable, and fully distributed. We will also briefly discuss the evolution from Milvus 1.0 to 2.0 and share various real-world use cases and applications.