After all the processes in the former three stages, we now have a well established data preprocessing pipeline and a correctly trained prediction model. The last stage of a machine learning system involves saving those resulting models from previous stages and deploying them on new data, as well as monitoring the performance, updating the prediction models regularly.
Best practices in the deployment and monitoring stage
Best practice 16 - save, load, and reuse models
When the machine learning is deployed, new data should go through the same data preprocessing procedures (scaling, feature engineering, feature selection, dimensionality reduction, and so on) as in previous stages. The preprocessed data is then fed in the trained model. We simply cannot rerun the entire...