Suppose you have written a cool machine learning (ML) app or created a shiny neural network model. Now you want to deploy this model over some web service or REST API. Or, you might have developed this model based on data streams coming from industrial sensors in a manufacturing plant and now you have to deploy the model on one of the industrial control PCs to serve decisions based on continuously incoming data. As a data scientist, an extremely common question that you may expect from the engineering/platform team is "how much memory footprint does your model/code have?" or "what's the peak memory usage by your code when running with some given data load?" This is natural to wonder about because hardware resources may be limited and one single ML module should not hog all the memory of the system.

Suppose you have written a cool machine learning (ML) app or created a shiny neural network model. Now you want to deploy this model over some web service or REST API. Or, you might have developed this model based on data streams coming from industrial sensors in a manufacturing plant and now you have to deploy the model on one of the industrial control PCs to serve decisions based on continuously incoming data. As a data scientist, an extremely common question that you may expect from the engineering/platform team is "how much memory footprint does your model/code have?" or "what's the peak memory usage by your code when running with some given data load?" This is natural to wonder about because hardware resources may be limited and one single ML module should not hog all the memory of the system. This is particularly true for edge computing scenarios i.e.