Nowadays, it’s relatively fast to deploy ML systems, but it is very easy to overlook how difficult and expensive it is to maintain those systems over time¹. I’m not an expert on the matter, so instead of trying to discuss every aspect of it, I want to talk about two issues:
Model Staleness: ML systems can encounter changing, non-stationary data. In these cases, if the model is not trained often enough to produce up-to-date models, the model is known to be stale².
Training/Serving Skew: Features might be calculated by different codepaths in different parts of the system. When these codepaths generate different values, this is known as training/serving skew².
Depending on the application’s nature and complexity, it is very important to understand the impact of both of these issues, and also how to detect and avoid (or mitigate) them.
So, as I show you how I deployed my application, I also want to discuss how those issues are present on this particular project, and how we can take them into consideration in our design.

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