I was asked to join the team running the National Transportation Data Challenge, not as a data scientist, but as a project manager to help keep all the big data people on track and moving forward. It’s an interesting use of my skillset and after a slow start I’ve been devoting more and more of my time to it. After lots of work behind the scenes, we showcased the Challenge to a more general public last week at JupyterCon in New York.
I left last week’s PyData Meetup with more questions than answers. Questions like “why does that neural net I just wrote perform the way it does?” So, with a couple of weeks left until the next project is due, I decided to go back and revisit the second half of the neural networks topic before moving forward.
Tonight I joined the first Southern California PyData meetup. It featured two speakers discussing how to better understand the predictions made by machine-learning models, and why it might be important to do so. I was impressed by the capabilities of the packages demonstrated and the likely importance of having such capabilities as we move forward with deep learning-based automation that could cause catastrophic results if it fails in unexpected ways.