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Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift. encouraging and rewarding) a culture of experimentation across the organization. Fragility occurs when a built system is easily “broken” when some component is changed.
Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”
It is entirely possible for an AI product’s output to be absolutely correct from the perspective of accuracy and dataquality, but too slow to be even remotely useful. Continuous retraining : a data-driven approach that employs constant monitoring of the model’s key performance indicators and dataquality thresholds.
Businesses are now faced with more data, and from more sources, than ever before. But knowing what to do with that data, and how to do it, is another thing entirely. . Poor dataquality costs upwards of $3.1 Ninety-five percent of businesses cite the need to manage unstructured data as a real problem.
Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. ” There’s either incomplete data, missing tracking data or duplicative tracking data, things like that.
These core leadership capabilities empower executives to navigate uncertainty, lead with empathy and foster resilience in their organizations. Success depends on understanding data needs, measuring ROI, fostering organizational AI fluency and partnering with ethically aligned ecosystems.
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