As it was in the beginning and now and ever shall be amen At the staff/principal level it’s all about maintaining “data impedance” between the product features

As it was in the beginning and now and ever shall be amen At the staff/principal level it’s all about maintaining “data impedance” between the product features that rely on inference models and the data capture This is to ensure that as the product or features change it doesn’t break the instrumentation and data granularity that feed your data stores and training corpus For RL problems however it’s about making sure you have the right variables captured for state and action space tuple and then finding how to adjust the interfaces or environment models for reward feedback