Architecture Artificial Intelligence CTO Machine Learning 
CIO
NOVEMBER 23, 2022
Enterprise architecture definition Enterprise architecture (EA) is the practice of analyzing, designing, planning, and implementing enterprise analysis to successfully execute on business strategies. Making it easier to evaluate existing architecture against long-term goals.
CIO
NOVEMBER 22, 2022
The next step in every organization’s data strategy, Guan says, should be investing in and leveraging artificial intelligence and machine learning to unlock more value out of their data. They’re having to integrate a massively expanding set of cloud-native services with existing on-premise technologies,” he notes.
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CIO
JANUARY 9, 2023
The need to grow smartly Gil Westrich’s company, ClearML, is benefiting from increased adoption of artificial intelligence and machine learning (ML) technology. But the CTO and co-founder says that scaling to meet that demand presents its own challenges, which require self-reflection.
CIO
DECEMBER 28, 2023
“IT leaders made regaining control of their cloud environments a top priority in 2023,” says Martin Hulbert, CTO of Ignite Technology, a software solutions provider based in the UK. On top of that, we implemented our AI factory, which is a workbench for our data scientists to develop machine learning systems with speed and quality.”
CTOvision
DECEMBER 28, 2015
We know you, dear readers, have been tracking the megatrend of artificial intelligence. The list is by no means comprehensive, but is definitely representative of what is available now: Artificial Intelligence On Your Phone: Siri : Part of Apple's iOS, watchOS and tvOS. Intelligent personal assistant.
CIO
OCTOBER 21, 2022
“We’re not 3,100 different companies but one restaurant 3,100 times, while each of those locations are impacted differently by their trade area, commute patterns, and weather patterns,” says Garner, who was promoted to CTO after proving his mettle. I never think of our architecture of static,” Garner adds. “I
CIO
APRIL 13, 2023
Deep learning models, for example, can have thousands or even millions of parameters. Model architecture can be simplified by reducing the number of layers in the neural network, which enables understanding and improves interpretation of the model’s decisions. Interpretability tools.
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