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3min chapter
Jordan Edwards: ML Engineering and DevOps on AzureML
Machine Learning Street Talk (MLST)
Machine Learning Model Risk Management
Many of these methods are open to interpret ation. And especially on unstructured models, they require another model just to classify whether or not do what you want. This is why i think we need to have some kind of model risk management process which will take into account the unique circumstances of every single model. To give you an example, in banking, when they do credit decisions, if you apply for a credit card, they won't use a model. Theyle use an expert e and the reason is it needs to be explainable, but not in the sense that you would think it.
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