Thine solution shall have at least general architecture documentation. Thine solution shall log what it did. Thine solution shall test it’s own behaviour for sanity. Thine solution’s results shall be monitored automatically. Do these seem familiar? For any software developer, of course they do. They have been around now for two decades. For data scientists … Read more
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
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