Machine Learning Model deployment using Spark

A while ago, I wrote a post about Productionizing Machine Learning Models, where I mentioned strategies for deploying Machine Learning models into production. This article is about one of the widely used approaches -- Batch Prediction using Spark. Using this approach, you can schedule a job to run the predictions at a specific time and output them to database / file systems / streaming / any persistent layer. With each prediction, there comes an overhead of pickling/unpickling the model. Another solution is to use tools like, Airflow and Perfect for batch predictions. Cloud solutions are also available for batch predictions like -- MLFlow and Amazon Sagemaker.