A holistic AI approach to generate long term benefits
Reliable and value-adding AI implementations in an industrial context are the lever to boost production effectiveness and product quality to the next level.
Machine Learning (ML) systems typically rely on static historic extracts of generally dynamic data for training. However, data and concepts may change rapidly – especially in an industrial environment.
In this paper, we explain our concept of how to continuously estimate the reliability of ML applications and generate actionable insights, by transitioning from static prototypes to managed ML services – automated, model-agnostic and scalable.
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