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IT leaders rethink talent strategies to cope with AI skills crunch

CIO

Now, they’re racing to train workers fast enough to keep up with business demand. Staffing strategies emerge Despite the continuously tight labor market and complexity of the task, Napoli believes he has Guardian Life’s AI talent strategy under control. Here’s how IT leaders are coping.

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NJ Transit creates ‘data engine’ to fuel transformation

CIO

The chief information and digital officer for the transportation agency moved the stack in his data centers to a best-of-breed multicloud platform approach and has been on a mission to squeeze as much data out of that platform as possible to create the best possible business outcomes. Data engine on wheels’.

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6 strategic imperatives for your next data strategy

CIO

According to the MIT Technology Review Insights Survey, an enterprise data strategy supports vital business objectives including expanding sales, improving operational efficiency, and reducing time to market. The problem is today, just 13% of organizations excel at delivering on their data strategy.

Strategy 265
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IT leaders’ AI talent needs hinge on reskilling

CIO

CIOs and HR managers are changing their equations on hiring and training, with a bigger focus on reskilling current employees to make good on the promise of AI technologies. As a result, organizations such as TE Connectivity are launching internal training programs to reskill IT and other employees about AI.

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What is Oracle’s generative AI strategy?

CIO

While Microsoft, AWS, Google Cloud, and IBM have already released their generative AI offerings, rival Oracle has so far been largely quiet about its own strategy. The service also comes with Nvidia’s foundation models, such as BioNeMo and Nvidia Picasso, along with AI training and governance frameworks.

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

Kubernetes can align a real-time AI execution strategy for microservices, data, and machine learning models, as it adds dynamic scaling to all of these things. However, a data execution strategy has to evolve for real-time AI to scale with speed. ML models need to be built, trained, and then deployed in real-time.

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Inferencing holds the clues to AI puzzles

CIO

Crunching mathematical calculations, the model then makes predictions based on what it has learned during training. Inferencing crunches millions or even billions of data points, requiring a lot of computational horsepower. The engines use this information to recommend content based on users’ preference history.