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

Executives from JP Morgan, Morgan Stanley, and BNY Mellon discuss the lessons learned through experimenting with machine learning at their firms.

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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The advantages of cloud-based services is well documented, from reduced upfront and ongoing operating and infrastructure costs to improved time-to-market for new services and datasets. Here, Tim Anderson, LSEG explains how the benefits of the service…

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