Best practices in the deployment and monitoring stage
After performing all of the processes in the previous 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 their performance and updating the prediction models regularly.
Best practice 19 – Saving, loading, and reusing models
When 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 the previous stages. The preprocessed data is then fed in the trained model. We simply cannot rerun the entire process and retrain the model every time new data comes in. Instead, we should save the established preprocessing models and trained prediction models after the corresponding...