MLOps

An exploration of some frameworks created by Google and Microsoft that can help think through improvements to how machine learning models get developed and deployed in production.

Use caches to save time in your training cycles, and potentially to save some money as well!

Eliminate technical debt with iterative, reproducible pipelines.

Short answer: not really, but it can become better!

MLOps isn't just about new technologies and coding practices. Getting better at productionizing your models also likely requires some institutional and/or organisational shifts.

The MLOps movement and associated new tooling is starting to help tackle the very real technical debt problems associated with machine learning in production.

Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.

Software engineering best practices have not been brought into the machine learning space, with the side-effect that there is a great deal of technical debt in these code bases.