Data Engineering Machine Learning Trends 
O'Reilly Media - Data
APRIL 11, 2018
It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.
O'Reilly Media - Data
JULY 18, 2018
Because large deep learning architectures are quite data hungry, the importance of data has grown even more. In this short talk, I describe some interesting trends in how data is valued, collected, and shared. Economic value of data. But if data is precious, how do we go about estimating its value?
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CIO
AUGUST 9, 2022
What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. The data engineer role.
CIO
APRIL 1, 2025
Job titles like data engineer, machine learning engineer, and AI product manager have supplanted traditional software developers near the top of the heap as companies rush to adopt AI and cybersecurity professionals remain in high demand.
CIO
MARCH 13, 2025
Gen AI-related job listings were particularly common in roles such as data scientists and data engineers, and in software development. Were building a department of AI engineering, mostly by bringing in people from data engineering and training them to work with gen AI and AI in general, says Daniel Avancini, Indiciums CDO.
Altexsoft
JULY 23, 2020
When speaking of machine learning, we typically discuss data preparation or model building. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. As a logical reaction to this problem, a new trend — MLOps — has emerged. This article. Better user experience.
CIO
NOVEMBER 27, 2024
For AI, there’s no universal standard for when data is ‘clean enough.’ A lot of organizations spend a lot of time discarding or improving zip codes, but for most data science, the subsection in the zip code doesn’t matter,” says Kashalikar. We’re looking at a general geographical area to see what the trend might be.
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