Deployment of Machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built.

When we think about data science, we think about how to build machine learning models. We think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate. However, how we are going to actually use those models is often neglected. And yet this is the most important step in the machine learning pipeline. Only when a model is fully integrated with the business systems, we can extract real value from its predictions.

To switch over from a development environment to a full-fledged production environment, an application needs to be deployed on a real web server.

We can productize the Keras model with Flask or Django.

https://www.udemy.com/deployment-of-machine-learning-models/ https://towardsdatascience.com/deploying-keras-deep-learning-models-with-flask-5da4181436a2