Turn Python Scripts into Beautiful ML Tools

In my experience, every nontrivial machine learning project is eventually stitched together with bug-ridden and unmaintainable internal tools. These tools -- often a patchwork of Jupyter Notebooks and Flask apps -- are difficult to deploy, require reasoning about client-server architecture, and don't integrate well with machine learning constructs like Tensorflow GPU sessions. I saw this first at Carnegie Mellon, then at Berkeley, Google X, and finally while building autonomous robots at Zoox. These tools were often born as little Jupyter notebooks: the sensor calibration tool, the simulation comparison app, the LIDAR alignment app, the scenario replay tool, and so on. As a tool grew in importance, project managers stepped in.