Combining rule engines and machine learning

You're getting two posts from Neal Lathia of Monzo this week! (It had been a while since I caught up on his blog...) This is a fantastic post about how to create hybrid ML/rules-based systems, which will often end up being both more robust systems and far simpler to build.

I recently wrote up an internal doc as a guide when there is appetite to add machine learning into an existing rule engine. This blog post pulls out three key questions from it: a) Can we use machine learning in a rule engine? b) How and where do we add the “machine learning bit?” c) How do we architect this type of system?

My takeaway: start with rules, replace certain decision nodes with narrow ML models when this improves performance of the system. I like this approach a lot, it's distinctly boring yet practical :)


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