Zilliz, the company behind the world’s most popular vector database Milvus, announced that Dr. Pan Yi joined Zilliz Research Asia (ZRA) as an AI pharmaceutical scientist.  

**SHANGHAI, Apr. 9, 2021** – Zilliz, the company behind the world’s most popular vector database Milvus, announced that Dr. Pan Yi joined Zilliz Research Asia (ZRA) as an AI pharmaceutical scientist.  

Dr. Pan is a pioneer in AI drug discovery. He is AIMBE (American Institute for Medical and Biological Engineering) Fellow, RSPH (Royal Society for Public Health) Member, IET (the Institute of Engineering and Technology) Fellow, JSPS (Japan Society for the Promotion of Science) Fellow, and Changjiang Scholar. He is also a Regents’ Professor of computer science, Associate Dean of College of Arts & Sciences, and the Chair of Biology with Georgia State University. 

Zilliz also released a plan to establish Zilliz Research Asia, a research subsidiary seeking to bring together a galaxy of data scientists, combine their academic research results with industry requirements, and drive innovation for the mainstream and emerging industries. Dr. Pan Yi will lead this initiative and continue to apply AI to pharmaceutical discovery.

Charles Xie, Founder and CEO of Zilliz said, “Professor Pan will undoubtedly boost Zilliz’s ability to optimize artificial intelligence algorithms, and help the pharmaceutical industry better understand unstructured data.”

“I see immense business potential in combining vector analytics with optimized AI algorithms in the area of new drug discovery,” said Pan Yi, “AI drug discovery is an emerging area. Once paired with optimized AI algorithms, Milvus improves efficiency and accuracy in areas far beyond drug discovery. I’m more than happy to make contributions that help realize the firm’s vision of success.”

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Dr. Pan Yi discussed challenges and opportunities for AI in bioinformatics and medicine and also described his recent research focus. He started with the common practices of the industry to optimize Convolutional Neural Networks (CNN), and then juxtaposed approaches his team used to improve the accuracy of AI algorithms, both theoretically and in real-world applications.

Pan Yi argued that prior knowledge or practical human knowledge can greatly improve the predictive ability of an AI model. It will require further study to identify the best approach for representing pratical human knowledge mathematically, and integrating such knowledge into AI models.

After Chinese tech giant Tencent [made its foray](https://www.globaltimes.cn/content/1194086.shtml) into new drug R&D using AI, Dr. Pan reiterated that the application of AI in pharmaceutical discovery is reaching new heights, and he will help accelerate the process of applying Zilliz’s unstructured data solutions to these scenarios.

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Prof. Pan Yi joins Zilliz Research Asia as an AI Pharmaceutical Scientist

We are proud to announce Milvus v1.0, a stable, long-term support version of the world’s most popular open-source vector database. Discover what Milvus v1.0 has to offer, and plans for the platform’s future.

Zilliz is proud to announce the release of Milvus v1.0. After months of extensive testing Milvus v1.0, which is based on a stable version of Milvus v0.10.6, is available for use.

Milvus v1.0 offers the following key features:

- Support for mainstream similarity metrics, including Euclidean distance, inner product, Hamming distance, Jaccard coefficient, and more.
- Integration with, and improvements to, SOTA ANNs algorithms, including Faiss, Hnswlib, Annoy, NSG, and more. 
- Scale-out capability through the Mishards sharding proxy.
- Support for processors commonly used in AI scenarios, including X86, Nvidia GPU, Xilinx FPGA, and more.

See the [Release Notes](https://www.milvus.io/docs/v1.0.0/release_notes.md) for additional Milvus v1.0 features. 

Milvus is an ongoing open-source software (OSS) project. Its first major release has the following implications for users:

- Milvus v1.0 will receive long-term support (3+ years).
- The most stable Milvus release to date is well structured and ready for integration with existing AI ecosystems.

### The first version of Milvus with long-term support

Thanks in part to sponsorship from Zilliz, the Milvus community will provide bug fix support for Milvus v1.0 until December 31st, 2024. New features will be available only in releases following v1.0.

See [The Milvus release guideline](https://milvus.io/docs/v1.0.0/milvus_release_guideline.md) for information about release cadences and more.

### Toolchain enhancements and seamless AI ecosystem integration

Beginning with v1.0, Milvus' toolchain will be a primary development focus. We plan to create the necessary tooling and utilities to meet the needs of the Milvus user community. 

Stability makes integrating Milvus with AI ecosystems a breeze. We are seeking further collaboration between the Milvus community and other AI-focused OSS communities. We encourage contributions to the new AI ASICs (application-specific integrated circuits) in Milvus. 

### The future of Milvus

We believe Milvus has a bright future thanks to the following factors:

- Regular contributions from developers in the Milvus community.
- Support for integration with any cloud-native environment.

We have drafted [community charters](https://milvus.io/docs/v1.0.0/milvus_community_charters.md) to help guide, nurture, and advance the Milvus community as our technology and user base grows. The charters include several technical decisions made to attract more participants to the community.

- Golang will now be used to develop the Milvus engine however, the ANNS algorithm component will still be developed in C++.
- The forthcoming distributed version of Milvus will use existing cloud components as much as possible.

We are thrilled to partner with the open-source software community to build the next-generation cloud data fabric made for AI. Let's get to work!

### Don’t be a stranger

- Find or contribute to Milvus on [GitHub](https://github.com/milvus-io/milvus/)
- Interact with the community via [Slack](https://join.slack.com/t/milvusio/shared_invite/zt-e0u4qu3k-bI2GDNys3ZqX1YCJ9OM~GQ).
- Connect with us on [Twitter](https://twitter.com/milvusio).

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Milvus v1.0 is available now. This stable, long-term support version powers image/video search, chatbots, and many more AI applications.

Milvus 1.0: The World's Most Popular Open-Source Vector Database Just Got Better

It is with great pleasure that we announce our recent research paper, Milvus: A Purpose-built Vector Data Management System, has been accepted by the 2021 ACM SIGMOD/PODS Conference.

![letter.png](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/letter_84e9cd57cf.png)

It is with great pleasure that we announce our recent research paper, *Milvus: A Purpose-built Vector Data Management System*, has been accepted by the [2021 ACM SIGMOD/PODS Conference](https://2021.sigmod.org/). The paper was recognized for its advanced work in vector data management, and its authors will be giving a keynote presentation at the SIGMOD Conference taking place from June 20-25 in Xi'an, China. Milvus was launched by Zilliz in 2019 and is currently an incubation-stage project at the LF AI & Data Foundation.

 

SIGMOD is the Association for Computing Machinery's Special Interest Group on Management of Data, which specializes in large-scale data management problems. The annual ACM SIGMOD Conference, which began in 1975, is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and to exchange techniques, tools, and experiences.  With an average acceptance rate of 17.4%, the annual ACM SIGMOD/PODS Conference is highly selective.

*Milvus : A Purpose-built Vector Data Management System* has been recognized by SIGMOD's review committee for introducing an end-to-end solution for vector similarity search. Milvus offers a modern design and implementation, such as query processing over structured attributes and multiple vectors, as well as improved heterogeneous computing efficiency. 
 

"This research offers a significant system design level breakthrough for vector data management," said SIGMOD's review committee. "Milvus supports real-time search on dynamic data and diversified search requirements that apply to a variety of business scenarios. As an open-source project, Milvus outperforms most competing vector databases. The committee found this research paper both inspiring and enlightening."

“As the world's first open-source vector database system, Milvus has developed rapidly over the past two years to become one of the most active, influential, and widely used projects under LF AI & Data Foundation,” said Charles Xie, Chairperson of LF AI & Data Governing Board, and Founder of Zilliz. “Milvus’ recognition by SIGMOD validates its technology and software architecture among academia, as well as the database industry at large. As an open-source project, Milvus has been successfully deployed in applications spanning new drug discovery, biometric analysis, and much more. I look forward to the ongoing and ever-evolving progress made by the Milvus project and its community, to propel AI unstructured data processing to new heights.”
 
<br/>

**About Zilliz**

Zilliz is the company behind Milvus, the world’s most popular vector similarity search engine, to accelerate the development of next-generation data fabric. Milvus is currently an incubation-stage project at the LF AI & Data Foundation and has been deployed by over 600 global users. The platform's efficiency and reliability simplify the process of deploying AI and MLOps at scale.

 Zilliz is on a mission to make sense of unstructured data through open-source and cloud-native solutions. Our technology has broad applications spanning new drug discovery, computer vision, recommendation engines, chatbots, and much more.




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New Zilliz Research on Vector Data Management Accepted by SIGMOD

AI-powered unstructured data processing and analysis startup Zilliz closed its latest Series B funding round with a $43 million investment led by Hillhouse Capital. 


**SHANGHAI, Nov. 13, 2020** – AI-powered unstructured data processing and analysis startup Zilliz closed its latest Series B funding round with a $43 million investment led by Hillhouse Capital. Additional investors include TBP Capital as well as continued participation from existing investors Morningside Venture Capital and Yunqi Partners. This marks the largest single Series B funding round for an open-source infrastructure software company to date.

Zilliz has raised over $53 million since its 2017 launch, and will use the latest round of funding to drive international workforce expansion, sustain and expand contributions to the open-source data science software ecosystem, advance development of enterprise-grade cloud services, and spur global adoption of AI-powered technology for unstructured data processing and analysis.

Unstructured data accounts for roughly 80% of all data, but only 1% gets analyzed due to processing complexities,” said Charles Xie, Founder and CEO of Zilliz. “Structured data, such as numerical values and text, has been efficiently handled by computers for decades. Unstructured data, things like images, videos, user behavior, chemical structures, and gene sequences, is far more ubiquitous. However, few tools are available to extract meaning from it. As digital transformation accelerates, unlocking the value of unstructured data will become progressively more essential. Our overarching goal at Zilliz is to utilize technology like heterogeneous computing and artificial intelligence to give people access to previously inaccessible insights.”

Xie continued, “Unstructured data processing and analysis has been pivotal to growing adoption of artificial intelligence worldwide, and we are committed to sustained investment in this space. We’ve found building software under the open-source model has eliminated national boundaries, brought a powerful network effect, and made analyzing unstructured data easier and more accessible for developers everywhere. Beginning late 2021, Zilliz will provide public, cloud-based unstructured data services that emphasize security, reliability, ease of use, and affordability."

Additional comments on the investment came from stakeholders at Hillhouse Capital, Morningside Venture Captial, and Yunqi Partners:

> Liming Huang, Investment Director, Technology Sector at Hillhouse Capital said, “How humans interact with each other, and our environments, is too complex to be represented by structured data. Hillhouse firmly believes leveraging embeddings similarity search to analyze massive unstructured datasets is the key to solving existing big data challenges. With Charles at the helm, Zilliz is building versatile applications that are a huge boon to the open-source data science software space and represent the future of data search.”

> Levi Liu, Partner at Morningside Venture Capital commented, “Three years ago Charles began ardently laying out his vision for meeting data processing demands in an increasingly digital future. We were compelled by the wide-array of real-world scenarios unstructured data analysis benefits, and have been repeatedly impressed by the innovation Zilliz is bringing to a variety of domains. We view this investment as the continuation of a long-term partnership.”

> Yu Chen, Managing Director at Yunqi Partners said, “Deep knowledge of database management, computer architecture, and the open-source software model make Charles an ideal fit for pioneering vector search technology. Zilliz’s product lines excel in almost every aspect including speed, scalability, and ease of use. We remain optimistic about future product developments, as well as the company’s ongoing contributions to global open-source communities.”

Zilliz was founded in 2017 and operates as a distributed team with offices in both the U.S. and China. The company specializes in the development of open-source, AI-powered unstructured data analysis software, and is the initiator and primary contributor to the vector similarity search project Milvus.

Milvus is currently an incubation-stage project at the LF AI Foundation, has been adopted by 400+ enterprise users, and has applications spanning image processing, computer vision, natural language processing (NLP), speech recognition, recommendation engines, search engines, new drug development, gene analysis, and more.


**Contact**

Please contact Jingyu Zhang by email at pr@zilliz.com.


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Zilliz Raises $43M in Series B Funding to Build AI-powered Unstructured Data Analytics

Keynote speakers addressed developing Faiss, applications of similarity search, and more. 

**BEIJING, Oct. 17, 2020**—Data science software company [Zilliz](https://zilliz.com), primary contributor to the open-source embeddings similarity search project [Milvus](https://www.milvus.io), hosted the first annual Milvus Community Conference last week in Beijing at the Wanda Vista Hotel. Over 200 people attended the event in-person, and more than 5,000 joined virtually.  

Event speakers included Matthijs Douze and Jeff Johnson, Research Scientists at Facebook AI; Charles Xie, Founder & CEO of Zilliz; and others. Presentations and Q&A sessions covered topics spanning the development of FAISS, applications of vector similarity search, solving unstructured data challenges with Milvus, and more. 

![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_1.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_1_a8f449759c.jpeg)
##### *2020 Milvus Community Conference attendees.*

#### Facebook AI researchers discuss similarity search and developing Faiss 
Matthijs Douze and Jeff Johnson, researchers at [Facebook AI](https://ai.facebook.com/research/), gave a joint conference session remotely. Facebook AI is part of Facebook’s larger machine learning group and operates as a distributed team with offices across the U.S. and Europe. The group researches topics including computer vision, conversational AI, natural language processing (NLP), recommendation engines, and more.  

Douze and Johnson are currently the primary developers of [Facebook AI Similarity Search (Faiss)](https://github.com/facebookresearch/faiss). Douze specializes in similarity search, embeddings, and unsupervised learning, while Johnson is an expert in low-level implementation of machine learning methods and optimizing AI algorithms for hardware efficiency. Johnson authored most of the original GPU backend for [PyTorch](https://pytorch.org), the primary neural network training library used and developed at Facebook. 

![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_2.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_2_1e71905f84.jpeg)
##### *Matthijs Douze and Jeff Johnson, Research Scientists at Facebook AI.*

Although not formally a research category at Facebook AI, similarity search has become more relevant as data science shifts from analyzing structured databases to embeddings. Increasingly image, video, audio, text, and more data types are being converted into embeddings, or fixed sized vectors with around 100 to 1,000 dimensions, then analyzed using machine learning. Converting raw data into embeddings enables analysis of massive, trillion-vector scale datasets that would otherwise be virtually impossible. 

Faiss began as an internal C++ library at Facebook but has since become a primary tool for indexing vectors after it was released under an open-source license in 2017. As authors of Faiss, Douze and Johnson spoke about the tool's functionality, ideal scenarios for using the CPU and GPU versions of Faiss, how to balance tradeoffs that come with similarity search, and more. For additional information, watch the [full presentation on Faiss](https://www.slidestalk.com/Milvus/FAISS?video=) from the 2020 Milvus Community Conference. 

#### Zilliz talks building Milvus, an embeddings similarity search engine powered by Faiss

Charles Xie, Founder and CEO of Zilliz, attended the conference alongside the company’s core R&D team. Zilliz is the primary contributor to Milvus, an open-source vector similarity search engine powered by Faiss and currently an incubation-stage project at the [LF AI & Data Foundation](https://lfaidata.foundation). Xie spoke about the project’s evolution, releasing Milvus under an open-source license in 2019, and future development plans. 
![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_3.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_3_5b835caae2.jpeg)

##### *Charles Xie, Founder and CEO of Zilliz.*

Hai Jin, Director of Research and Development at Zilliz, discussed the unique functionality Milvus builds on top of Faiss. According to Jin, as a cloud-native application with support for different indexes (HNSW, Annoy, etc.), more efficient resource utilization, and other database management optimizations, Milvus is a more powerful and adaptable tool than Faiss on its own. 

![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_4.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_4_767a325185.jpeg)

##### *Hai Jin, Director of Research and Development at Zilliz.*

####  Watch the 2020 Milvus Community Conference in full 

Additional speakers presented real-world applications of using Milvus to make sense of unstructured data. This included building a video recommendation engine at leading Chinese video streaming site iQIYI, creating a registered trademark search engine at Beijing-based online housing platform Beike, and more. Watch the [2020 Milvus Community Conference](https://www.slidestalk.com/w/298) in its entirety to see these sessions and learn how vector similarity search solves modern big data challenges. 


#### More pictures: 

![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_5.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_5_4f7c1cefb7.jpeg)

![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_6.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_6_3489cf3530.jpeg)

![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_7.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_7_ab7b191290.jpeg)

![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_8.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_8_6a71fbcfd0.jpeg)

![News_Facebook AI Researchers Discuss Similarity Search at 2020 Milvus Community Conference_9.jpeg](https://zillizstorage.blob.core.windows.net/zilliz-assets/zilliz-assets/assets/News_Facebook_AI_Researchers_Discuss_Similarity_Search_at_2020_Milvus_Community_Conference_9_3b348f0d74.jpeg)

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