Rolling Out a Python Machine Learning Model to Production Using a Java based REST api
Motivation Once your data was prepared for Machine Learning algorithms, and your model is trained and fine-tuned. Now it is time to launch your solution. In the previous blog post , I focused on the steps presented above. Using python scikit-learn library, I showed you how to build a model. Now that the model is ready, how can you exposed the model knowledge to other applications? Approach This post presents the steps you should follow once you are ready to roll out a production deployment. To this end, I will expose the model using a REST api. But I will propose one more challenge. Beside the fact Python has great solutions to expose a REST api, imagine that you production environment allows you to deploy using only Java, as an example. The machine learning team has to be able to delivery a model which will be executed using a Java Virtual Machine. Exporting the Model The first post showed the steps to build model (xgb_clf). As you can see below, I am exporting the m