Different Architectures of Machine Learning Model Deployment!

Machine Learning Model Deployment Architecture signifies how a Machine Learning Model is deployed or the design pattern that is used to deploy the machine learning model. Any model that is deployed, in every case, is deployed with some application because a model will be deployed to fulfill some use case, & the presentation of that use-case or at least the designing of the interface that is used to deploy the model will be done using any application. For example, the simplest model deployment can be done through a web page that can take input from the user, then take that input to the model (API working), & then return the result to the user. Here, the application will be that simple web page. That being said, let's understand the 4 different architectures of model deployment: In the architecture, the model is deployed within the application in an embedded way as a dependency of the application, the model is packaged within the final/consuming application at the build time of the application.