Model Deployment in Production – Validated models are deployed in production. Now various applications can call the model to get predictions by sending scoring requests over technology-independent standard protocols like Rest/HTTP. There could be two types of deployments – online (synchronous access) and batch (asynchronous access). The online deployment of a model needs model execution runtime to run continuously so that the model can be accessed in a synchronous manner for a single prediction request (or a small set of prediction requests, aka micro-batch). This is typically used in use cases that need prediction from the model in real-time; for example, online transaction fraud prediction, intent identification for chatbots, etc. Batch deployment of models needs an infrastructure that can spawn the runtime on-demand and stop when predictions for all batch scoring requests are generated.

Editor's note: Sourav Mazumder is a speaker for ODSC West 2021. Be sure to check out his talk, "Operationalization of Models Developed and Deployed in Heterogeneous Platforms," for more info on trustworthy AI there. Artificial intelligence (AI) is already having a significant impact on the development of humanity, already. For enterprises, the use of AI is not an option anymore. However, the core of AI relies on the use of data samples/examples to train a system/machine using algorithms so that it can behave intelligently like a human.

Digital twin technology continues to be adopted by manufacturing industries to support business strategy and gain efficiencies in operations and customer service. Definition: Digital twin refers to a digital replica of potential and actual physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. Digital twins are used for a wide variety of use cases. Manufacturers use digital twins to help them reduce maintenance costs on machinery and optimize production output. For example, by analyzing and experimenting with the virtual copy, manufacturers don't have to take down physical operations to test and implement updates.