As is obvious, data-driven solutions built or run with crappy data will not create business value, at worst, they do actual damage to business or people. Solving the issue is not hard. Problem is that too large a portion of data science sprint deliveries are measured by quantity, not by quality of deliveries. “How many … Read more
This is obvious. And yet, teams go on a-building even after seeing the data to be too crap to answer the questions in mind. In the age of anyone with internet connection being able to get their AI terminology right, why do people still… …train machine learning models with crappy data? The model(s) will be … Read more
We design and implement data assets on-site and in-cloud(s) for machine learning and AI; from data extractions, transformations and loading, automated (and on-demand) data quality audits, assessment and monitoring and statistics and multivariate modeling. See more in LinkedIn