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Data engineers vs. data scientists

O'Reilly Media - Data

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.

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The future of data: A 5-pillar approach to modern data management

CIO

It was not alive because the business knowledge required to turn data into value was confined to individuals minds, Excel sheets or lost in analog signals. We are now deciphering rules from patterns in data, embedding business knowledge into ML models, and soon, AI agents will leverage this data to make decisions on behalf of companies.

Data 167
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IT leaders: What’s the gameplan as tech badly outpaces talent?

CIO

But 76% of respondents say theres a severe shortage of personnel skilled in AI at their organization, according to the August report. In a November report by HR consultancy Randstad, based on a survey of 12,000 people and 3 million job profiles, demand for AI skills has increased five-fold between 2023 and 2024.

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What is a data engineer? An analytics role in high demand

CIO

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.

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Are you ready for MLOps? 🫵

Xebia

Gartner reported that on average only 54% of AI models move from pilot to production: Many AI models developed never even reach production. Universities have been pumping out Data Science grades in rapid pace and the Open Source community made ML technology easy to use and widely available. Why bother with MLOps? What a waste!

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NJ Transit creates ‘data engine’ to fuel transformation

CIO

Data engine on wheels’. To mine more data out of a dated infrastructure, Fazal first had to modernize NJ Transit’s stack from the ground up to be geared for business benefit. Today, NJ Transit is a “data engine on wheels,” says the CIDO. We have shown out value,” Fazal says of the transformation.

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MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

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. MLOps lies at the confluence of ML, data engineering, and DevOps. More time for development of new models.