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5 machine learning essentials nontechnical leaders need to understand

TechCrunch

We’re living in a phenomenal moment for machine learning (ML), what Sonali Sambhus , head of developer and ML platform at Square, describes as “the democratization of ML.” Consider upskilling your current team of software engineers into data/ML engineers or hire promising candidates and provide them with an ML education.

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AI data readiness: C-suite fantasy, big IT problem

CIO

The survey points to a fundamental misunderstanding among many business leaders regarding the data work needed to deploy most AI tools, says John Armstrong, CTO of Worldly, a supply chain sustainability data insights platform. Gen AI uses huge amounts of energy compared to some other AI tools, he notes.

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

CIO

Gen AI-related job listings were particularly common in roles such as data scientists and data engineers, and in software development. They can certainly educate internally, but the technology is evolving so rapidly that by the time you finish a grad school course or program, the technology is different.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning - AI

The flexible, scalable nature of AWS services makes it straightforward to continually refine the platform through improvements to the machine learning models and addition of new features. Dr. Nicki Susman is a Senior Machine Learning Engineer and the Technical Lead of the Principal AI Enablement team.

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Data Scientist vs Data Engineer: Differences and Why You Need Both

Altexsoft

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.

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Highlights from JupyterCon in New York 2018

O'Reilly Media - Data

Watch keynotes covering Jupyter's role in business, data science, higher education, open source, journalism, and other domains, from JupyterCon in New York 2018. Machine learning and AI technologies and platforms at AWS. Watch " Machine learning and AI technologies and platforms at AWS.". Democratizing data.

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IT leaders rethink talent strategies to cope with AI skills crunch

CIO

Moreover, many need deeper AI-related skills, too, such as for building machine learning models to serve niche business requirements. He wants data scientists who can build, train, and validate models for use cases, and who can perform exploratory analysis and hypothesis testing. Everyone is learning,” Daly says.