Case study · Life Sciences
ML Platform for Life Sciences
MLflow-based platform supporting regulated clinical and commercial ML workloads.
Key results
- Models in production +6x
- Experiment reproducibility 100%
- Regulatory audit posture established
Context
A pharmaceutical company's ML work was distributed across research teams running notebooks on local workstations with no shared experiment tracking, no model registry, and no reproducibility guarantee. Regulated workloads (clinical-data-adjacent models) required a posture the current setup didn't provide.
Challenge
The ML platform had to serve both regulated (clinical) and non-regulated (commercial) ML workloads without forcing the commercial team into regulated-overhead and without letting the clinical team skip the regulatory controls.
Approach
Thoughtwave delivered a 10-month ML platform build: MLflow for experiment tracking and model registry, feature store, deployment automation, regulatory-workload isolation, and governance workflow aligned to GxP expectations. The 10-month engagement covered architecture, platform build, team onboarding across both research and commercial, and first regulated-workload deployment.
Outcomes
Production model count improved 6× as teams moved from notebook-only to platform-deployed models; experiment reproducibility reached 100% because the platform captured the lineage; regulatory audit posture established — the first clinical-adjacent workload deployed successfully.
Want a similar engagement?
We deliver engagements like this one across AI, data analytics, cybersecurity, and workforce solutions. Bring your scenario; we bring the team and the production patterns.