Meeting the Requirements of Data Scientists and AI Developers
For NVIDIA, a key component of supporting agencies amid this strategic shift is satisfying the requirements of data scientists on one hand and machine-learning operations and DevSecOps on the other. Their needs may differ, but it’s important that they can coexist.
“IT departments need to provide the platform for end users to do their work,” Cvetanov says.
A common example is building a new AI application, whether for process automation or data analysis.
“The IT team has mature resource management tools, but part of the challenge is they’ve never had to manage an AI cluster,” Cvetanov says. “AI workloads have a very specific set of requirements for performance and latency that are oftentimes very different from traditional enterprise applications.”
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In these scenarios, the ideal approach is to consider the workload in the context of the agency’s existing tools and processes.
“We want to make the learning curve less steep. We don’t want our customers to have to onboard a lot of new tools if they can get away with using the ones they are already trained on,” Cvetanov says. “For instance, if an AI workflow is tested and proven to work well in a virtual environment without significant performance degradation, then they can use their existing tooling to manage the GPU environment, as we have full integration with platforms like VMware.”
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