기업내에 AI/ML를 적용하기 위해, 업무 관점에서 시민 데이터 사이언티스트(Citizen Data Scientist, 이하 CDS)와 그 필요 역량인 데이터 문해력(Data Literacy)의 중요성이 높아지고 있습니다.(참고 : 데이터 문해력과 시민 데이터 사이언티스트의 필요 역량) 이와 연결하여, 데이터를 기반으로 신속하게 개발한 예측 모델을 업무 시스템에 통합 또는 활용하기 위해 IT 관점에서 해결해야할 과제와 접근 방안에 대해
Between DevOps, DataOps, MLOps and ModelOps, there are different "Ops" based on different environments. "Ops" generally is the shortened version of Operations. Check out some of the different ones in our current technological world. How many do you know? Learning about DevOps DevOps or Developer Operations refers to applying agile
DataOps increases the productivity of AI practitioners by automating data analytics pipelines and speeding up the process of moving from ideas to innovations. DataOps best practices make raw data polished and useful for building AI models. Models need to work on the data that is introduced, as well as on
Whether working as a business analyst, data scientist or machine learning engineer, one thing remains the same – making an impact with data and AI is what really matters. Pre-processing and exploring data, building and deploying models and turning those scoring values into an actionable insight can be overwhelming. A
DataOps is rapidly turning from a fragmented usage of some tools popular in the software development world into a modern approach to data & analytics engineering with its own best practices and recommended technologies. While the goal of DataOps – delivering data and analytical insights of the highest quality faster
Editors note: This is the first in a series of articles. According to the Global McKinsey Survey on the State of AI in 2021, the adoption of AI is continuing to grow at an unprecedented rate. Fifty-six percent of all respondents reported AI adoption – including machine learning (ML) –
Kim Kaluba explains how connecting data to decisions helps create resiliency.
Jim Harris explains the relevance of DevOps, DataOps and ModelOps for data analytics practitioners.
David Loshin gives CIOs 4 suggestions for shaping successful digital transformation initiatives.
"Practical AI" might seem like an oxymoron to some. But that’s only if you view artificial intelligence as a futuristic and unrealistic pursuit. Kirk Borne, PhD, decidedly does not. Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In this
K(o)ennen Sie schon „DevOps“? Machen Sie SAS? Dann lohnt sich eventuell ein frischer Blick auf die Kombination! Denn immer mehr Unternehmen probieren, ihren produktiven Betrieb auch in die Hände der Software-Entwickler zu legen (2 von 3 laut Jenkins) – speziell in der Analyse, insbesondere beim agilen Modellieren und dem Veredeln
