How to Build a Search Engine from Scratch?

Whether it is to browse the web or to shop the latest trends, search engines make our lives easier, and we use them on a daily basis. However, no matter how effortless they appear, their creation is anything but easy. In reality, at large retail corporations, search is handled by tens or even hundreds of world-class engineers, product managers and data scientists who support the development, improvement and maintenance of the entire system. In this talk, I will explain how retail search is different than web search. I will also dig into the different algorithms and pieces that come into play, emphasizing the main reasons why a high-performance engine is very challenging to develop.

Dr. Jennifer Prendki is the Head of Data Science at Atlassian, where she leads all Search and Machine Learning initiatives and is in charge of leveraging the massive amount of data collected by the company to load the suite of Atlassian products with smart features. She received her PhD in Particle Physics from University UPMC - La Sorbonne in 2009 and has since that worked as a data scientist for many different industries. Prior to joining Atlassian, Jennifer was a Senior Data Science Manager in the Search team of Walmart eCommerce. She enjoys addressing both technical and non-technical audiences at conferences and sharing her knowledge and experience with aspiring data scientists.