Bayer Built a System to Run ML Workflows. Then Explanation Became the Bottleneck.
According to AWS, Bayer Crop Science runs hundreds of machine-learning model configurations across crops, regions and teams, and each run needs to be repeatable and governed. To meet that requirement, Bayer built the Connected Decision Engine, or CDE, a system for deploying, orchestrating, monitoring and reusing ML workflows. CDE gives breeders, analysts, researchers, data scientists and data engineers a consistent way to run analytics while preserving execution history and operational controls.
As adoption grew, the bottleneck moved from running workflows to understanding them. Explaining a result meant piecing together context from APIs, documentation, metadata stores, dashboards and logs.
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The interesting move is where the work goes: the bottleneck is no longer running the workflow but reconstructing why it produced a result, often by digging across separate systems.
That makes “preserving execution history and operational controls” sound less like bureaucracy and more like the raw material for any later explanation.