Senior Data Engineer
Ian is a Senior Data Engineer at Lucidworks. As part of the Platform team, he has been helping to integrate the cutting-edge solutions developed by the AI-Lab team into Lucidwork's Fusion product to work at high-volume production scales. He has also written a book for O'Reilly titled Programming PyTorch for Deep Learning, introducing readers to common Deep Learning ideas and techniques.
Ian Pointer is speaking at the following session/s
Implementing a Deep Learning Search Engine
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.
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.
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.