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What is Metaflow's explicit support for Transfer Learning tasks? In other words, how do I know what models to use or not use? I am surmising from the techblog p
by troelsSteegin 6y ago
What is Metaflow's explicit support for Transfer Learning tasks? In other words, how do I know what models to use or not use? I am surmising from the techblog post that there is a stable set of content-intrinsic features, and that can be separated from perhaps more dynamic features-sets that characterize audiences, presentation treatment, and viewing (as conditioned on all the other stuff). But it sounds like there is a stable set of features for prediction tasks, as well, which is to say that for a task like predicting an audience for movie X in region Y, you'll need some set of features, and that we have some set of trained models (and recommend analytic components) available that match some or all of those features for this task. Is that a "thing", or is the workflow support simpler than that, and should that be a "thing"?
- vtuulos 6y agoGood question! What you are asking is pretty much the core question for a certain set of ML tasks at Netflix. Metaflow is rather unopinionated about those types of questions, since they are subject to active research and experimentation. Metaflow aims to make it easy to conduct the research and experiments but it is up to the data scientist to choose the right modeling approach, features etc. In some cases, individual teams have built a thin layer of tooling on top of Metaflow to support specific problems they care about. I could imagine such a layer for specific instances of transfer learning, for instance. In general, we are actively thinking if/how Metaflow could support feature sharing in general. It is a tough nut to crack.