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Various new essential features of a vector database are provided together with the release of Milvus 2.0. Among the new features, Time Travel, attribute filtering, and delete operations are correlated as these three features are achieved by one common mechanism: bitset.
Therefore, this article aims to clarify the concept of bitset in Milvus and explain how it works to support delete operations, Time Travel, and attribute filtering with three examples.
Milvus is an ongoing open-source software (OSS) project focused on building the world's fastest and most reliable vector database. New features inside Milvus v1.1.0 are the first of many updates to come, thanks to long-term support from the open-source community and sponsorship from Zilliz. This blog article covers the new features, improvements, and bug fixes included with Milvus v1.1.0.
Like any OSS project, Milvus is a perpetual work in progress. We strive to listen to our users and the open-source community to prioritize the features that matter most. The latest update, Milvus v1.1.0, offers the following new features:
Milvus is an open-source vector database designed to manage massive million, billion, or even trillion vector datasets. Milvus has broad applications spanning new drug discovery, computer vision, autonomous driving, recommendation engines, chatbots, and much more.
In March 2021, Zilliz, the company behind Milvus released the platform's first long-term support version — Milvus v1.0. After months of extensive testing, a stable, production-ready version of the world's most popular vector database is ready for prime time. This blog article covers some Milvus fundamentals as well as key features of v1.0.
Cybersecurity remains a persistent threat to both individuals and businesses, with data privacy concerns increasing for 86% of companies in 2020 and just 23% of consumers believing their personal data is very secure. As malware becomes steadily more omnipresent and sophisticated, a proactive approach to threat detection has become essential. Trend Micro is a global leader in hybrid cloud security, network defense, small business security, and endpoint security. To protect Android devices from viruses, the company built Trend Micro Mobile Security — a mobile app that compares APKs (Android Application Package) from the Google Play Store to a database of known malware. The virus detection system works as follows:
To work, the system has to perform a highly efficient similarity search on massive vector datasets in real-time. Initially, Trend Micro used MySQL. However, as its business expanded so did the number of APKs with nefarious code stored in its database. The company’s algorithm team began searching for alternative vector similarity search solutions after quickly outgrowing MySQL.
Building machine learning (ML) applications is a complex and iterative process. As more companies realize the untapped potential of unstructured data, the demand for AI-powered data processing and analytics will continue to rise. Without effective machine learning operations, or MLOps, most ML application investments will wither on the vine. Research has found that as little as 5% of the AI adoptions companies plan to deploy actually reach deployment. Many organizations incur "model debt," where changes in market conditions, and failure to adapt to them, result in unrealized investments in models that linger unrefreshed (or worse, never get deployed at all).
This article explains MLOps, a systemic approach to AI model life cycle management, and how the open-source vector data management platform Milvus can be used to operationalize AI at scale.
Milvus is an open-source vector similarity search engine powered by approximate nearest neighbor search (ANNS) algorithms such as Faiss, NMSLIB, and Annoy.
To make a vector search more intuitive and convenient, we introduced TableFile and metadata in Milvus.