The platform has already been adopted in local production by thousands of companies around the world, including Lucidworks, Shutterstock, and Cloudinary. In this webinar, Zilliz engineers will highlight the changes and features included with Milvus 2.0, a variety of use cases and applications in production—and how they empower small and large users alike.


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Meet Up

Milvus 2.0, an open-source similarity search engine

Vector similarity search is powerful. It can extract valuable insights from complex information and make relevant recommendations from abstract inputs. But to be useful, it has to be fast. Whether suggesting products or surfacing valuable information, results often need to be served instantaneously.

How can you ensure similarity search is speedy, even when querying massive million, billion, or even trillion vector datasets? We've partnered with Lucidworks, an enterprise search technology company, to explain how the company used Milvus to build semantic search at speed. Join our webinar and discover how Milvus, an open-source vector database, makes it possible for Lucidworks to search an entire vector space in milliseconds.

Accelerating Semantic Search for AI-Powered Data Discovery with Milvus & Lucidworks

Searching big data is complex. Traditional keyword-based search methods are poorly suited for processing and analyzing unstructured data such as images, text, video, and audio that do not follow a predefined model. Vector similarity search utilizes deep learning methods and ANN (approximate nearest neighbor) algorithms to provide faster, more accurate search capable of processing trillion-scale unstructured datasets. This talk focuses on vector indexing, which plays a major role in accelerating time-consuming queries on large datasets. We will cover how various index types can be used in [Milvus](https://www.milvus.io/), a popular open-source vector similarity search engine, as well as Milvus v1.0 features and future plans for the platform.

What is vector similarity search? How to get a competitive edge using vector indexing? 

Learn about Milvus v2.0 and the groundbreaking functionality developed by the Milvus core team. v2.0 includes feedback from over 1000+ enterprise users and 10,000 community members in order to build a better database to serve enterprises in more AI and big data scenarios.

After gathering this feedback and invaluable use cases, we are pleased to launch Milvus v2.0.

This version has made significant progress in Milvus as a cloud native and distributed system. In this talk, we will introduce the main features, functionality and future prospects of Milvus v2 .0

