Building sustainable, adaptable systems
We have looked at the step-by-step processes for model governance and sustainable model training and deployment. We also now understand how important it is to build reusable feature stores.
We understand that without a feature store, we will end up with a separate feature engineering pipeline for each model that we want to deploy. Duplicate pipelines inevitably lead to added compute costs and data lineage overheads, as well as lots of engineering effort. However, the endeavor of building a sustainable feature store will be fruitless if it’s not robust and resilient enough to adapt to data and concept drift.
Even when we design large-scale distributed ML systems, we should think about building an adaptable system with the ability to detect data drift, concept drift, and calibration drift. This will facilitate continuous monitoring and mean that we can manage new, incoming data from different sources. For example, in a retail system...