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Dataiku
IQVIAIQVIA Uses MLOps for Feature Pipelines
IQVIA, a health care organization in the United States, uses MLOps from Dataiku to support applied machine learning for data scientists and ML engineers.
Shared MLOps workspaces help IQVIA keep applied machine learning honest across teams.
“Inside IQVIA, applied machine learning used to depend on whoever still had the latest file.”
“Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Productivity | Handoffs in applied machine learning sit in a shared queue instead of a mailbox trail |
| Risk and compliance | MLOps is the governed place data scientists and ML engineers use for applied machine learning |
| Capability | New joiners can see how applied machine learning actually runs |
Story
Inside IQVIA, applied machine learning used to depend on whoever still had the latest file. That pattern is common in health care groups working out of the United States. Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.
IQVIA uses MLOps from Dataiku as the working layer for feature pipelines. Dataiku is a universal AI platform that lets data teams and analysts collaborate on pipelines, models, and governed AI applications. The practical change is simple: applied machine learning has a home, and reviews happen there instead of in a forwarded thread.
Nothing in this writeup invents a savings number. What IQVIA gets from Dataiku is a durable place to run applied machine learning and a way for data scientists and ML engineers to see the same feature pipelines at the same time.
