What is the operational complexity of your ML model?

FAQs for AI Zero → One (Episode #4)

Transcript:

Welcome everyone to episode four of unraveling AI entrepreneurs. This is a series for engineers and product managers where we cover frequently asked questions on challenges in the domain of AI and going from an idea to real-world products. Today’s question is about the operational complexity of your ML model, and the key point here to understand is not all ML models are equally complex when it comes to running them in production. Let me give you a few examples.

Let’s say you've written an ML model that generates an email every night recommending, you know, articles for, you know, your users to read, for instance. So it’s more of an offline email generated once in 24 hours. That operational [00:01:00] complexity is very different, from considering, an ML model which is being used in real-time gaming where it is actually learning from the actions of the player and adapting to the next steps or the next challenges it kind of shows within the game. That’s a whole different extreme in terms of the operational complexity to have that model [00:01:30] in production.