Estimating Uncertainty in Machine Learning Models -- Part 1

"We demand rigidly defined areas of doubt and uncertainty!" Let's imagine for a second that we're building a computer vision model for a construction company, ABC Construction. The company is interested in automating its aerial site surveillance process, and would like our algorithm to run on their drones. We happily get to work, and deploy our algorithm onto their fleets of drones, and go home thinking that the project is a great success. A week later, we get a call from ABC Construction saying that the drones keep crashing into the white trucks that they have parked on all their sites.