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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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10 most in-demand generative AI skills

CIO

If any technology has captured the collective imagination in 2023, it’s generative AI — and businesses are beginning to ramp up hiring for what in some cases are very nascent gen AI skills, turning at times to contract workers to fill gaps, pursue pilots, and round out in-house AI project teams.

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How companies around the world apply machine learning

O'Reilly Media - Data

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Platforms sessions. Privacy and security.

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What is Machine Learning Engineer: Responsibilities, Skills, and Value Brought

Altexsoft

In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. But no technology can work efficiently without human experts behind it.

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What is a data architect? Skills, salaries, and how to become a data framework master

CIO

Data architect role Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles, often in support of data or digital transformations. Data architect vs. data engineer The data architect and data engineer roles are closely related.

Data 316
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Henkel embraces gen AI as enabler and strategic disruptor

CIO

Like many incumbents in the consumer packaged goods (CPG) industry, Henkel was slow to embrace digital technologies, resulting in a widening disconnect between the 147-year-old company and the changing needs of its customers. We’ve been lucky, I think, because we have interesting industry problems to crack,” Nilles says.

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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. Much less often the technology is mentioned in terms of deployment. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. More time for development of new models.