Isn't anything but neural networks obsolete?

Hi there,

deep neural networks are great, especially for high dimensional and rather unstructured data (like pictures, video frames, …). Also benefits like transfer learning can be really great!

But often interpretability and explainability are a crucial business requirement. You might not only quantify statistically how certain the model is, maybe you also want to know how and why the model came to the conclusion.

So if you already have very good domain knowledge and can I corporate this into your features, resulting in a smaller feature space (e.g 5 dimensions or so) maybe another model can meet your requirements even better than a NN w/ less data (which is usually much more cost-efficient, considering your labelling strategy).

Let’s take Gaussian processes (GPs) for example.
There are a few reasons why one might choose to use a (GP) over a deep neural network (DNN) like interpretability or explainability reasons as well as exploiting the opportunity to incorporate prior knowledge into the model which can provide uncertainty estimates in addition.

Note: as Andrew pointed out, the model itself might not represent your biggest leverage. Often improving the data quality or let’s say data-centric AI can be the stronger lever to excel in your application considering business requirements, see also: A Chat with Andrew on MLOps: From Model-centric to Data-centric AI - YouTube

Best regards