For organizations, the topic of how to adopt frameworks like model performance management (MPM), which is used to instill confidence in AI systems, is a pressing one. Should we build it ourselves or just make use of already-existing resources and infrastructure?
It is frequently necessary to close the feedback cycle of your models to make continual updates to your optimization algorithm in machine learning in order to stay up with the drift of your data. This concept originates from control theory and is used to enhance model performance by comparing predicted and actual model outputs.
- Feedback loops are an essential part of control theory, and there are two main types: open and closed.
With an open loop, the system’s prior output is ignored. Past results are evaluated by closed loops as they process fresh inputs to the system.
The MLOps lifecycle includes model performance management as a closed-loop feedback mechanism. The model becomes closed-loop when it incorporates the information from the model performance management system regarding the model’s output. Model results are compared to externally-sourced ideal or anticipated results (also known as desired references). Results from this validation procedure may then be analyzed by your team to provide clues as to how to go about improving the accuracy of the model’s predictions. At the beginning of the MLOps lifecycle, system feedback may assist your company to increase modeling optimization, and in the later phases, it can help enhance forecasts.