CIO
MAY 1, 2024
But getting from potential to profitability does not come without risks, such as assuming that your established processes for deploying mainstream enterprise IT infrastructure will work in the new era of complex AI superclusters. A solid technology infrastructure has always been essential. Yet, AI systems don’t operate the same way.
CIO
OCTOBER 30, 2023
CIOs are responsible for much more than IT infrastructure; they must drive the adoption of innovative technology and partner closely with their data scientists and engineers to make AI a reality–all while keeping costs down and being cyber-resilient. An estimated 90% of the global datasphere is comprised of unstructured data 1.
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CIO
MAY 2, 2024
A seismic shift is underway in the evolution of the data center, driven by a variety of converging factors. Whether integrating cutting-edge AI capabilities trained on proprietary data or leveraging pre-built functionalities, the ability to optimize and automate workflows becomes effortless with a platform-centric approach.
CIO
JULY 28, 2023
There is an explosion of personal data about what we buy, where we go, and what we watch. We trust the custodians of our data to ensure it is not breached or used irresponsibly. But not all organizations that store and process sensitive customer data are fully aware that a chink in infrastructure can break our digital trust.
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Machine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability. What are the core elements of an MLOps infrastructure? Why do AI-driven organizations need it?
CIO
OCTOBER 20, 2023
But adopting modern-day, cutting-edge technology is only as good as the data that feeds it. Cloud-based analytics, generative AI, predictive analytics, and more innovative technologies will fall flat if not run on real-time, representative data sets.
TechCrunch
DECEMBER 14, 2020
Big data is a sham. There is just one problem with big data though: it’s honking huge. Processing petabytes of data to generate business insights is expensive and time consuming. Processing petabytes of data to generate business insights is expensive and time consuming. data governance) based on local privacy laws.
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In today’s ambitious business environment, customers want access to an application’s data with the ability to interact with the data in a way that allows them to derive business value. After all, customers rely on your application to help them understand the data that it holds, especially in our increasingly data-savvy world.
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