Experimentation Risk Uncertainty 
O'Reilly on Data
MARCH 25, 2025
The coordination tax: LLM outputs are often evaluated by nontechnical stakeholders (legal, brand, support) not just for functionality, but for tone, appropriateness, and risk. ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs).
Rocket-Powered Data Science
FEBRUARY 15, 2023
3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). encouraging and rewarding) a culture of experimentation across the organization. Test early and often.
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O'Reilly on Data
MARCH 31, 2020
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.”
O'Reilly on Data
SEPTEMBER 15, 2020
Technical competence results in reduced risk and uncertainty. AI initiatives may also require significant considerations for governance, compliance, ethics, cost, and risk. Results are typically achieved through a scientific process of discovery, exploration, and experimentation, and these processes are not always predictable.
O'Reilly on Data
DECEMBER 9, 2019
Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions. and an error term ??
Domino Data Lab
MAY 15, 2019
Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. It is more experimental because it is “an approach that involves learning from data instead of programmatically following a set of human rules.”
The Unofficial Google Data Science Blog
APRIL 23, 2024
Crucially, it takes into account the uncertainty inherent in our experiments. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. In this section we’ll discuss how we approach these two kinds of uncertainty with QCQP.
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