Sept. 11, 2023, 9 a.m. |

InfoWorld Machine Learning www.infoworld.com



Data wrangling, dataops, data prep, data integration—whatever your organization calls it, managing the operations to integrate and cleanse data is labor intensive. Many businesses struggle to integrate new data sets efficiently, improve data quality, centralize master data records, and create cleansed customer data profiles.

Dataops isn’t a new challenge, but the stakes are higher as more companies want to become data-driven organizations and leverage analytics as a competitive advantage. Digital trailblazers are also extending dataops into unstructured data sources to …

ai and machine learning artificial intelligence businesses challenge customer customer data data data integration data management dataops data prep data quality data sets integration labor machine machine learning master master data operations organization profiles quality records software development

Senior Machine Learning Engineer

@ GPTZero | Toronto, Canada

ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)

@ HelloBetter | Remote

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Cloud Data Platform Engineer

@ First Central | Home Office (Remote)

Associate Director, Data Science

@ MSD | USA - New Jersey - Rahway