Data Collection Metrics Testing Uncertainty 
Occam's Razor
APRIL 8, 2013
To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.
O'Reilly on Data
MARCH 31, 2020
Machine learning adds uncertainty. The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. Underneath this uncertainty lies further uncertainty in the development process itself.
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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.
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. It is similar to R&D. Transcript.
Occam's Razor
JULY 12, 2010
Please click on the above image for a higher resolution version , including all the other metrics.]. In the last month data was copied off one of my posts 5,616 times, with most of it being content and some of it images. Click on the image for a higher resolution version , along with a peek at other metrics.]. Why is this cool?
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