In today's corporate world, AI and machine learning models are increasingly becoming a critical part of business strategy. To ensure that these models are not only successfully implemented, but also effectively managed and utilized, the introduction of a governance framework is crucial.
In this article, you can read about the key aspects of such a framework, which focuses on compliance and governance meta-information, technical performance indicators and business metrics.
To ensure sustainable success, it is necessary to integrate a continuous feedback loop into the AI lifecycle, which serves as a trigger for re-training and model adjustments based on the metrics mentioned above, but also compares the economic contribution of a model against its costs for operation, infrastructure and maintenance.
In this context, we present our standardized process model for implementation, starting with strategic alignment and definition of metrics through to technical integration, e. g. via a governance layer in your IT landscape.