This year is the first time that Booz Allen and NVIDIA have partnered to enter a team into the Data Science Bowl. Our goal for this combined team was to share some of our successes and challenges along the way, as well as to provide insight into how to approach this type of competition. We’ve been able to post updates about our progress, respond to questions on the Kaggle forums, and help other teams find new ways of looking at the problem. Of course, we’re also hoping that by combining our talent and resources we will be able to come up with a top solution – even if we’re not eligible for the prize money.
Whereas Jon and Peter have previously provided some insight into how we’re tackling the challenge, I want to touch on why we’re here. While we each had different reasons for joining the team, we were all excited to get our hands on a data set that is not only hard to come by, but has the potential to really impact people’s lives. In our day-to-day work we don’t always get to contribute to real-world, lifesaving use cases like the one provided by this year’s DSB, and it is an opportunity we are all excited about. I personally had the added incentive to join the BAH/NVIDIA DSB team in order to learn more about complex image analysis and machine learning techniques and to participate in my first Kaggle competition! Getting to work with pros like Max, Peter, Jon, and Jared have made it an enjoyable learning experience for me so far.
Another reason for our participation was the opportunity to work as part of a team that is outside of our normal network of colleagues. A challenging part of this is that we are geographically dispersed and working in different time zones, but we have found that the use of Github and Slack to share code and communicate with each other in a timely manner has been successful. We also hold weekly team phone calls. To anyone who is new to Kaggle competitions or would like more advice on how to structure and work effectively on a dispersed team, please reach out in the forums!
—Written by Samantha Tracht