2 Comments
Feb 11Liked by Aurimas Griciūnas

Thank you for the post!

The graphs are very clear as always and you have definitely inspired me to use them more in my work!

I have couple of discussion points regarding the business logic and ML services:

1. I am a proponent of bundling ML code with business logic as it is usually coupled anyways - rules depend on the output OR rules are very light wrapper in which case it does not even matter that much where to put them (maybe). However, I have recently think of a situation where it might be worth separation - when ML deployment is done through some kind of managed service (e.g. MLFlow on Databricks cloud). This way, we can keep the business logic services pointing to the specific versions of the model. I think this brings some nice things to the table - for example it is easier (in my view) to run several versions of the ML model simultaneously. What is your take?

2. Another point is about feature extraction. You have them accessed from the ML service (in a separated case), but my intuition would be to keep that service as close to "pure function" as possible. Mainly - all the input should come with the "request". Do you see any issues with this approach?

3. And the last point concerns you example about the business logic stitching together result from multiple ML models. I am wondering more about team structures (team topologies) that enables this parallel work. Given that this whole peace made up of three parts needs to produce consistent and good results - therefore those components are coupled. How do you handle this then? Have seen good examples?? From my experience, the same team owned all those services and would deploy the whole pipeline usually even just one part changed - basically the whole pipeline was treated as a single unit, but it was distributed because some parts could use a lot smaller machines than others.

Once again, thank you for writing. There is such an array of interesting topics and discussions!

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