Data Collection Data Quality Metrics Uncertainty 
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.”
The Unofficial Google Data Science Blog
JULY 18, 2023
Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. Last, we’ll provide a case study of how xRR can be used to measure improvements in a data-labeling platform.
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Domino Data Lab
JUNE 23, 2019
Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. That’s another pattern.
IBM Big Data Hub
SEPTEMBER 11, 2023
Government executives face several uncertainties as they embark on their journeys of modernization. A pain point tracker (a repository of business, human-centered design and technology issues that inhibit users’ ability to execute critical tasks) captures themes that arise during the data collection process.
Domino Data Lab
JULY 22, 2019
One is data quality, cleaning up data, the lack of labelled data. You know, typically, when you think about running projects, running teams, in terms of setting the priorities for projects, in terms of describing, what are the key metrics for success for a project, that usually falls on product management.
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