Engineering Practices for Machine Learning Lifecycle at Google and Microsoft

As demands for AI applications grow, we've seen a lot of effort put by companies to build their Machine Learning Engineering (MLE) tools tailored for their needs. There are just so many challenges faced by industries in regards to having a well-designed environment for their Machine Learning (ML) lifecycle: building, deploying, and managing ML models in production. This post will cover two papers, explaining MLE practices from two of the leading tech companies: Google and Microsoft. Adding a little bit of context, this article is part of a graduate-level course at Columbia University: COMS6998 Practical Deep Learning System Performance taught by Prof. Parijat Dube who also works at IBM New York as Research Staff Member. The first section will present a paper from Google and will touch on the building part of an ML lifecycle.