If your project requires deployment to be tested on live data, you should create a web application or a REST API to be used across all platforms(web, android, iOS). Major steps (which would vary depending on the project) include:

Save your final trained model into an h5 or pickle file
Serve your model using web services, you can use Flask to develop these web services
Connect the input data sources and set up the ETL pipelines
Manage dependencies using pipenv, docker/Kubernetes(based on scaling requirements)
You can use AWS, Azure, or Google Cloud Platform to deploy your service
Monitor the performance on live data or simply for people to use your model with their data

ML application transition from development to production.

*click the image to enlarge:

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Pau Labarta Bajo’s Post

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