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Implementing a Deep Learning Search Engine

Presented at virtual Activate 2020. Recent advances in Deep Learning brings us the possibility to get improvements in almost any domain. Search Engines aren’t an exception. Semantic search, visual search, “zero results” queries, recommendations, chatbots etc. – this is just a shortlist of topics that can benefit from Deep Learning based algorithms. But more powerful methods are also more expensive, so they require addressing the variety of scalability challenges. In this talk, we will go through details of how we implement Deep Learning Search Engine at Lucidworks: what kind of techniques we use to train robust and efficient models as well as how we tackle scalability difficulties to get the best query time performance. We will also demo several use-cases of how we leverage semantic search capabilities to tackle such challenges as visual search and “zero results” queries in eCommerce.

Sava Kalbachou, AI Research Engineer, Lucidworks
Ian Pointer, Senior Data Engineer, Lucidworks

Intended Audience:
Engineers, Data Scientists, Product Owners and just Machine Learning enthusiasts who want to enrich their products with the DL-powered semantic search capabilities. Prerequisite knowledge isn’t necessary although might be useful to understand some concepts in deep.

Attendee Takeaway:
You will learn how DL-based semantic search solutions can drastically improve the search experience for you and your users yet still being scalable and applicable in the production.

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