![]() Not only is this an ethical concern but also a legal-compliance issue as AI and ML become more regulated. It is increasingly common to find ML predictive models embedded in automation workflows to facilitate automated decision-making, for instance.Īlthough learning from historical data or classifying and predicting scenarios is beneficial, ML techniques have not always been subject to the same level of transparency, audibility, and interpretability as their process-automation counterparts.īeing able to assess, understand, debug, and benchmark AI and ML models is a fundamental issue when used in processes that could directly impact business decisions and people's lives. Top considerations for building a modern edge infrastructureĪrtificial intelligence (AI) and machine learning (ML) are becoming prevalent in modern life, including in business decisions and process automation.How to explain edge computing in plain English.
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