Do these seem familiar? For any software developer, of course they do. They have been around now for two decades. For data scientists taking their quick-draft Proof-of-Concept live quickly, these seem to be too often totally alien concepts. Documentation: Writing 100 pages of documents, which become outdated in a week as the software develops are … Read more
It is not surprising that startups with promiseware are hit when the market gets risky; some investors who were pseudo-rich with a stock portfolio, have now been punched financially so hard they are closing their outbound money-streams to survive, and pull funding. But. Companies with strong working products (like grocery trade) realize they don’t need … Read more
Today Finland decided to close it’s borders, schools, cultural public mass gathering places such as libraries, the National Opera and such to protect the weak and the old from getting infected with a virus potentially deadly to them. The part of Prime Minister Sanna Marin’s speech, which caught a data-man’s ear, was that testing for … 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
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
First touch with “ADLA” was one of familiarity (lo, ye olde YARN and a DAG-implementation, I salute you) and a thought of “you guys will come up with something better, soon”. Why the skepticism? Well, the combination of SQL and C# gave programmability…but for who? IMHO: ADLA was mostly a quick-fix from Azure to serve … 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
New decade, new implementations with Spark, Azure Data Lake and Synapse Analytics on Azure just now on the table. Blog posts on these later.