Walking by the hottest IT streets in these days means you've likely heard about achieving Streaming Machine Learning, i.e. moving AI towards streaming scenario and exploiting the real-time capabilities along with new Artificial Intelligence techniques. Moreover, you will also notice the lack of research related to this topic, despite the growing interest in it.
If we try to investigate it a little bit deeper then, we realize that a step is missing: nowadays, well-known streaming applications still don't get the concept of Model Serving properly, and industries still lean on lambda architecture in order to achieve the goal. Suppose a bank has a concrete frequently updated batch trained Machine Learning model (e.g. an optimized Gradient Descent applied to past buffer overflow attack attempts) and it wants to deploy the model directly to their own canary.