Rishabh Mehrotra currently is Head of AI at Sourcegraph. Previously he worked as a Director of Machine Learning at ShareChat based in London. His current research focuses on machine learning for marketplaces, multi-objective modeling of recommenders, and the creator ecosystem. Prior to ShareChat, he was an Area Tech Lead and Staff Scientist/Engineer at Spotify where he led multiple ML projects from basic research to production across 400+ million users. Rishabh has a PhD in Machine Learning from UCL, and 50+ research papers and patents. Some of his recent work has been published at conferences including KDD, WWW, SIGIR, RecSys, and WSDM. He has co-taught a number of tutorials and summer school courses on the topics of learning from user interactions, marketplaces, and personalization.
This is my second course from Uplimit and I am really happy with the number of new things I learned. The material was quite in-depth and the projects were rather challenging but quite fulfilling. A lot of material on recommenders can really only be found in research papers and the team at Uplimit has managed to condense a lot of that into a 4-week course, which you could never get anywhere else.
It's been amazing to learn from an industry expert in RecSys himself. Rishabh and Uplimit team structured the course in such a way that the salient details are covered really well. The pragmatic touch through projects was a cherry on top! I would definitely suggest anyone who has an interest in implementing to the deploying their recommender systems at scale to take this course!
Rishabh is an expert in recommendations and you can feel his passion for the field throughout the course. We covered some of the hottest aspects of recommendations nowadays in a very hands-on manner 🙂.
If you have some experience with Recommender Systems, you will find Personalized Recommendations at Scale taking you to the next level. The course introduces you to various concepts relevant to industrial systems and it will help you understand them through practical exercises at the end of each week. The community is constructive and you will find many experienced members helping you throughout the projects and make good connections while you learn and have fun.
Great course that was approachable enough for most practitioners while still getting deep into the weeds about state-of-the-art ongoings in recommendation.
