Introduction Nowadays it is easy to build - train and tune - powerful machine learning (ML) models using tools like Spark, Conda, Keras, R etc. The business value of these models, however, only comes from deploying the models into production. Deploying Machine Learning models in production is still a significant challenge. There is no general strategy that fits every ML problem and/or every company’s need. Deployment can be done in wide variety of ways, which entails either loading the model directly into the application or making API’s and calling them from the application.