It’s not popular to do “online learning” for a variety of reasons. u/CabSauce gave a nice list. One reason I wanted to add was that many models are exposed to relatively uncontrolled input and that can backfire badly. Google “Microsoft Tay Twitter” for a cautionary tale. Garbage in garbage out; letting your model learn in an uncontrolled environment risks inputting (lots of) garbage, and sometimes even malicious/adversarial data. Making matters worse, since the garbage affects the model in real time the actively-getting-worse predictions just get made/published/used in a production setting.
In most cases the upside to continuous learning is small compared to batched releases, but it makes a lot of stuff harder and more risky.
teb311 t1_j8qdjqv wrote
Reply to [D] Is anyone working on ML models that infer and train at the same time? by Cogwheel
It’s not popular to do “online learning” for a variety of reasons. u/CabSauce gave a nice list. One reason I wanted to add was that many models are exposed to relatively uncontrolled input and that can backfire badly. Google “Microsoft Tay Twitter” for a cautionary tale. Garbage in garbage out; letting your model learn in an uncontrolled environment risks inputting (lots of) garbage, and sometimes even malicious/adversarial data. Making matters worse, since the garbage affects the model in real time the actively-getting-worse predictions just get made/published/used in a production setting.
In most cases the upside to continuous learning is small compared to batched releases, but it makes a lot of stuff harder and more risky.