Hexagon-ML powers data science collaboration at scale
One of the largest nonprofit healthcare plans in the United States required a platform solution for their annual internal data science competition.
The challenge? To scale their analytics quotient effortlessly while also fostering collaboration in a siloed environment — which often led to low participation amongst employees.
In 2018, we joined forces with them and hosted the first internal data science competition using Hexagon-ML’s platform. The results? Competition setup time was reduced from 4 weeks to just 30 minutes and employee participation increased by a massive 84%. Since then, our platform has facilitated thousands of data science employees collaborating through our rich discussion forum, and data and code sharing tools.
In 2019, Hexagon-ml helped IBM Research launch one of the first Reinforcement Learning competitions.
Thanks to Hexagon-ml's structured approach and well-defined processes, a total of 250+ teams and 735 submissions were recorded. We aim to continue this success and look forward to hosting the NeuroIPS Reinforcement Learning challenge in December 2022.
IBM Research uses Hexagon-ml to streamline their competition experience
Hexagon-ml wins innovation award at KDD Cup
In 2019, Hexagon-ml led the first reinforcement learning competition for KDD, the annual Data Mining and Knowledge Discovery competition. Partnering with Oxford university and IBM Research, we created a new humanities track and bagged the innovation award for KDD Cup 2019.
Hexagon-ml hosts Multi-dataset Time Series Anomaly Detection
In 2021 Dr. Eamon Keogh partnered with us to develop and host the first ever "Multi-dataset Time Series Anomaly Detection" competition as part of KDD 2021. This goal of this competition was to encourage industry and academia to find a solution for univariate time-series anomaly detection. Prof. Keogh has provided 250 data-sets collected over 20 years of research to further this area. Please review the brief overview video developed by Dr. Keogh.