Machine Learning Architecture

How do we deploy machine learning algorithms? Let’s go beyond the Jupyter notebook. This is by no means an exhaustive list. However, this article outlines a couple of the major paradigms, high level architectures and related rools. For each type of deployment, I have listed some typical architecures and use cases of those architecutres. I’ve also included a list of tools to investigate alongside those architectures.

On Call

Machine Learning Deployments that are “on call” are designed to provide predictions only on a ocassional basis. “On Call” deployments make predictions on groups of observations at one time. The predictions are then stored in a database for downstream usecases.

On Demand

Predictions services are always available and provide predictions in real time.

On Edge

Predictions services are deployed on the device without connecting to any external services or applications.

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