Deployment ML-OPS Guide Series - 2

The most exciting moment of any machine learning system is when you get to deploy your model, but deploying becomes hard due to statistical issues such as "when past model performance is no more guaranteed for future and model performance degrade over a period of time due to changes of data when the model is deployed in a cloud with frequent data changes" and system engine such as system demands monitoring the ML system often which is manual in nature and tedious which needs to be handled through automation as much as possible. Now, How to deal with the statistical issue or degrading performance of the model?. How to handle the data changes once the model is deployed? That is where Concept and Data drift comes into the picture. Concept Drift refers to if the desired mapping from x to y changes and it leads to inaccurate predictions due to huge data distribution changes in the productized model.