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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