When machine learning goes awry, here's how to do better next time

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At this year’s North American Financial Information Summit, held on May 17 in Manhattan, machine-learning engineers from JPMorgan, BNY Mellon, and Morgan Stanley discussed where they’ve seen machine-learning (ML) projects go wrong. While some of the lessons learned from failures may seem obvious, inexplicable, opaque ML implementations still plague the industry. Financial professionals want to use ML as a magic bullet, but without a thought-out process and a plan, the end results often

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