What is the most efficient way of serialising the machine learning models?

There are three ways of serialising machine learning models in Python. These are JSON, Pickle and Joblib. However, Joblib is the most efficient way of serialising the machine learning models because it is stores large multi-dimensional numpy arrays efficiently. Scikit-learn estimators represent model parameters in the form of numpy arrays. Hence it makes sense to use joblib for…

How do you serialise and deserialise machine learning model after training?

It is important to serialise the models for its later use for prediction on unseen data. Think of serialisation just as storing the machine learning model in form of a file. That file should have model itself, preprocessing object scikit-learn(or any other) version, and testing accuracy if possible. Instead of maintaining model and preprocessing object…

How do you deploy machine learning models in production?

Machine learning models can be deployed on production in the following way and in order: HTTP endpoint used by the application like an app or a web UI to get the prediction.  Web server running web applications behind the http endpoint with one of the following options: Web hosting frameworks   Flask Django Serverless compute AWS…