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.

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Meet Up

Enhancements Milvus v2.0

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



Introducing Milvus v2.0

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? 

Topics for the meeting include: support for query/search return vector raw data, support for strings in Milvus 2.0, support for deletion in Milvus 2.0, and Milvus Insight and it's roadmap.


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Live

Milvus Technical Meeting

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.


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

This session is being presented as a part of the [Future of Data Science & AI](https://hubs.la/Q01D9l2W0) virtual conference hosted by Data Science Dojo on March 1-2, 2023.

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. 

In this tutorial, we’ll introduce embeddings and vector search from both an ML- and application-level perspective. We’ll start with a high-level overview of embeddings and discuss best practices around embedding generation and usage. We’ll then use this knowledge to build two systems: semantic text search and reverse image search. Finally, we’ll see how we can put our application into production using Milvus, the world’s most popular open-source vector database.



Unlock the Power of Embeddings with Vector Search

Join Frank Liu at Open Source Summit North America for The Rise of Vector Databases - Lessons From the Milvus Community.

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: the vector database.

In this presentation, we'll introduce some of the interesting production use cases we've seen with Milvus - the world's most popular open source vector database. Additionally, we'll discuss pitfalls and challenges associated with integrating Milvus into a production data/ML stack. Finally, we'll wrap up with a discussion on ongoing community feedback and future plans for Milvus.

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