In the last couple of years, I’ve had the opportunity to improve some highly-used software systems applying recent developments in machine learning (ML). Amid all the joys and the noise in developing and deploying ML models in production, there is a nagging discontinuity in both the thought-process and actual software development which has felt unnatural to me. The discontinuity is between the Java/C++/Python/Go programs we write as part of standard software development and the artifacts of ML i.e. learned models. In this post, I will quickly describe the problem and a potential opportunity to address it using differentiable programming.