Okay, let's make it clear at the start: This post is NOT intended for people who are doing one-off, siloed projects like participating in Kaggle competitions or doing hobby projects on Jupyter notebooks to learn the trade. The value of throw-away, quick, dirty script code is obvious there — and has its place. Rather, it is intended for ML practitioners working in a production setting. So if you’re working in an ML team that is struggling to manage technical debt while pumping out ML models, this one’s for you.

A typical workflow

It is now widely agreed that reproducibility is an important aspect of any scientific endeavor. With Machine Learning being a scientific discipline, as well as an engineering one, reproducibility is equally important here.

There is widespread fear in the ML community that we are living through a reproducibility crisis. Efforts like the Papers with Code Reproducibility Challenge, signaled a clear call-to-action for practitioners, after a 2016 Nature survey revealed that 70% of results are non-reproducible.

While a lot of the talk amongst the community has centered on reproducing machine learning results in research, there has been less focus on the…

Predictive maintenance has diffused through the cycle of innovation to the extent that it is highly probable that you have heard about the term in one way or another. While the jury is still out on what the best practices to exactly implement predictive maintenance are, there is no doubt of the business value that drives much of the fan-fare around the buzzword.

This, coupled with the increasing accessibility of the machine learning toolbox to handle large scale sensor data, has led to many attempts in building ML pipelines for predicting failures and optimizing maintenance schedules. …