[habibur]

> a machine learning model is trained that takes in all these dozens of features and spits out a score (details on how such a model is trained to be covered in the next post).

This part was the one I was interested in. As most of the rest are obvious.

[nikhilgarg28]

(Disclaimer: I'm the author of the post)

Good feedback, noted. Will get the next post focused on training within the next couple of days.

[1minusp]

Also, how is this article different or more informative compared to others that deal with the challenges of model deployment/management at scale?

[nikhilgarg28]

Model deployment is an important but still tiny part of the overall ranking/recommendation systems. Bulk of the complexity stems from two key properties of recommendation systems (which are different from say computer vision models): 1. The system operates on user feedback. As a result, it needs to manage flow of lots of data, with at least some subset being managed in realtime.

2. For any single request, there are thousands of things to recommend from. As a result, a single request is not scoring a single ML model but thousands of models - one (or often more, see value modeling the post) for each candidate.

[arkj]

Looks like FAANG in the title is just to get your attention. Details are missing.