moving-toward-a-citizen-data-science-model 
Dataiku
MAY 10, 2021
When picturing a data scientist, many people might imagine a highly skilled individual working alone. Indeed, data scientists are often very specialized and it is therefore challenging for individuals to replicate their capabilities in order to increase the benefits businesses receive from such skills.
CIO Business Intelligence
MAY 28, 2022
Das’s biggest priorities include integrating Randall-Reilly IT with client systems to become more essential to their businesses, rearchitecting old systems using modern approaches such as microservices, implementing AI and machine learning to automate manual processes, and delivering clean, standardized data for analytics and monitoring.
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Birst BI
AUGUST 23, 2018
However, there are simply not enough data scientists in the world to deliver on the AI potential. Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics. The Outsourcing of Data Science Functions.
CIO Business Intelligence
MAY 25, 2022
Das’s biggest priorities include integrating Randall-Reilly IT with client systems to become more essential to their businesses, rearchitecting old systems using modern approaches such as microservices, implementing AI and machine learning to automate manual processes, and delivering clean, standardized data for analytics and monitoring.
Domino Data Lab
FEBRUARY 20, 2019
Eugene Mandel , Head of Product at Superconductive Health , recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science. Eugene Mandel , Head of Product at Superconductive Health , recently dropped by Domino HQ to discuss cross-team collaboration within data science.
Domino Data Lab
APRIL 29, 2021
Summary: APIs will get better at transferring model components from one application to another and transferring pipelines to production. If we can crack the nut of enabling a wider workforce to build AI solutions, we can start to realize the promise of data science. Let’s start with the themes. Image from zazzle.com.
O'Reilly on Data
OCTOBER 19, 2021
As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Why: Data Makes It Different. However, the concept is quite abstract. Can’t we just fold it into existing DevOps best practices?
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