In simple terms, deployment of any machine learning model should start from identifying the application that we want to build.
Second, we should understand the data requirements for the model and then gather the data. After that, we need to identify our model and train the model with the data and test for accuracy of prediction of the model.
After that, we need to create a new web application using the appropriate framework, for example, the most common Python-based web application framework is Flask. Then, we need to complete the code or rather put the code in some repository like GitHub.
We need to create an account in any of the service providers specialized in providing a platform as a service and the common being Heroku. After that, we need to link the account that we have created in Heroku with that of the GitHub.
Heroku is a free platform which can be used to you create the API for the machine learning model that we have created. We need to understand that there are requirements of the certain libraries for running the code in the Heroku platform and environment.
