Finding metrics that describe performance can unlock valuable insights in the field of Data Science. It can be helpful to visualize the distribution of these metrics and to understand how segments of metrics vary with each other. It is needed here to define categories such as age or gender that can divide the data, which is a limitation of segmenting analytics. Vector Search uses semantics to analyze data, and does not have the limitation of requiring symbolic tags. In this talk, you will learn how to use Vector Search as a Data Scientist. By means of real Youtube and Twitter data, you’ll see how easy it is to utilize this yourself with the Vector Search engine Weaviate.
Connor Shorten, Research Scientist @ SeMI Technologies
Connor is a Research Scientist at SeMI Technologies, where he works on the Weaviate Vector Search Engine. He is thrilled about the opportunity of Vector Search to extend Database functionality! Connor was originally introduced to Vector Search while researching his publication “Deep Learning applications for COVID-19”. As a part of his Ph.D. research group at FAU, including the FAU College of Nursing and the Memorial Healthcare System, Connor will present his work on Vector Search for personalized treatment planning. Connor is also an avid content creator, having published over 300 YouTube videos on Henry AI Labs which have accumulated roughly 2 million views and 40,000 subscribers. Connor is currently continuing this work with the Weaviate Podcast. He will be presenting how Vector Search can aid in content performance analytics.
Vector Search for Data Scientists