Portfolio

What we’ve
shipped.

MLOps and Deep Learning deployments from notebooks to production. Healthcare, fintech, and infrastructure work. Every project on this page is live and running.

6+
Projects completed
2
Clinical models in prod
GCP+AWS
Cloud platforms
4–8 wks
Avg delivery time
Case studies
Medical CT scan used for brain tumour Deep Learning detection model deployed by Clavenix MLOps
Healthcare • Deep Learning

Brain tumour detection deployed to radiology workflow

CT scan-based Deep Learning model, DICOM pipeline, HIPAA audit logging, and delayed ground truth monitoring in a live clinical environment.

Chest X-ray for COVID classification Machine Learning model production deployment
Healthcare • MLOps

COVID classification from chest X-rays in production

Clinical load testing, HIPAA-compliant inference, and monitoring thresholds designed for radiological ground truth latency.

Fintech trading screens showing fraud detection Machine Learning Engineering deployment
Fintech • Machine Learning Engineering

Real-time fraud detection on AWS SageMaker

Sub-20ms inference serving, automated retraining on concept drift, and SOC2-aligned audit trail for a Series B payments platform.

MLOps pipeline architecture on GCP Vertex AI data center infrastructure
Architecture • MLOps

Multi-model MLOps platform on Vertex AI Pipelines

Shared feature store, unified monitoring dashboard, and automated retraining across 6 Deep Learning models for a healthcare analytics company.

Credit risk scoring Deep Learning model deployed from notebook to production for fintech
Fintech • Deep Learning

Credit risk scoring: notebook to production in 5 weeks

End-to-end Machine Learning Engineering for a lending platform. Shadow deployment, canary release, and full data lineage for regulatory reporting.

HIPAA compliant ML inference architecture reference for healthcare AI systems
Architecture • HIPAA

HIPAA-compliant inference architecture reference

Private endpoints, mTLS service-to-service, input hash logging, immutable audit trails, and field-level encryption for ePHI-touching ML systems.

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