How Jina Saves Your Time on Building Cloud-Native Neural Search Systems

Today, with the ever more long documents and multimedia data, finding the right information is more important and challenging than ever. The rise of deep learning has ushered in a new era of “neural search”. However, building a neural search system is non-trivial work for many engineers. The main challenges are: (1) long dev cycle due to the complex tech stack (2) poor scalability due to the glued-architecture (3) strong requirements on the domain knowledge to fine-tune the results. With Jina (https://github.com/jina-ai/jina), engineers can quickly build up a search engine powered by state-of-the-art AI in just minutes. In this talk, I will introduce the design philosophy and the key features of Jina; and showcase how Jina bootstraps a QA semantic search system and a short-video search system in just lines of code.

Dr. Han Xiao (https://www.linkedin.com/in/hxiao87/) is the Founder & CEO of Jina AI. Han has worked in AI OSS for quite some time. His Fashion-MNIST and bert-as-service were listed as the most popular AI open-source projects in 2017&18 world-widely. In 2018-2020, Han led a team on neural information retrieval at Tencent AI, laying down the next-gen search infrastructure. Han served in the Tencent Technical Advisory Council and Opensource Program Office, fostering the open-source and DevOps culture inside the company. Han served as a board member at LF AI Foundation in 2019, driving the open source innovation in AI by enabling international collaboration. In 2014-2018 Han worked at Zalando Research in Berlin as a Senior Research Scientist. Han received the Ph.D. (2014) and M.Sc. (2009) in computer science from the Technical University of Munich in Germany. He is also the Founder & Chairman of the German-Chinese Association of AI, an NPO in Germany.

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