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Dataiku@Honor

Honor Runs Applied Machine Learning with Dataiku

Honor, an information technology organization in China, uses Govern from Dataiku to support applied machine learning for data scientists and ML engineers.

Information Technology teams at Honor treat Dataiku as the working layer for applied machine learning.

“From China, information technology teams still had to serve customers and internal partners who expected a straight answer.”
“Dataiku is a universal AI platform that lets data teams and analysts collaborate on pipelines, models, and governed AI applications.”
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Independent write-up. Figures come from public sources or are illustrative.

Value results

CategoryValue result
CapabilityFeature pipelines can be reviewed without waiting on a personal export
CapabilityNamed workflow replaces ad hoc routing for feature pipelines
CapabilityApplied machine learning stays visible to adjacent teams through Govern

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Honor grew feature pipelines faster than the local tools around it. From China, information technology teams still had to serve customers and internal partners who expected a straight answer. Data scientists and ML engineers were the ones stitching the picture together by hand.

Rolling out Govern put applied machine learning on Dataiku. Dataiku is a universal AI platform that lets data teams and analysts collaborate on pipelines, models, and governed AI applications. Honor keeps the product in the path where work already happens, so data scientists and ML engineers do not context-switch into a graveyard system used only for audits.

The visible result is steadier applied machine learning. Feature pipelines is easier to inspect, and adjacent groups can join data scientists and ML engineers without a guided tour of someone's desktop.

Value Graph

Honor uses Dataiku, SentinelOne, Cloudflare, Nutanix, VMware, Ansys. Shared with Agilent Technologies, American Express GBT, Anglo American, Asahi Kasei, ASE Technology. Industry: Information Technology. Value: Capability. Drag nodes, filter types, or expand a node to follow more commonalities.

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