How do you handle regulatory compliance?

Encrypted artifact storage, audit logs for all model accesses, and immutable experiment records.

What was the platform cost?

$150k/year for 10 data scientists, replacing 6 months of manual work per model.

How does this case study work?

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Building End-to-End MLOps Platforms

Executive Summary

A healthcare AI startup took 6 months to develop each diagnostic model—too slow for clinical validation. ML engineers built a unified MLOps platform with data versioning, experiment tracking, and automated deployment, reducing development time to 3 weeks.

Key Outcomes

▹ 6 months → 3 weeks per model
▹ 5 models in production vs 0 before
▹ $10M in clinical partnerships secured

Client Situation

Data scientists worked in silos with no shared infrastructure. Each model took months to reach production.

Key Challenges

⚠ No experiment tracking—duplicate work common
⚠ Manual data versioning causing reproducibility issues
⚠ Deployment took 2+ months after model ready

Existing Architecture

Local Jupyter notebooks, manual data downloads, email for model handoff to engineering.

Experiments not reproducible
Models deployed inconsistently
No monitoring or drift detection

Solution Design

End-to-end MLOps platform: DVC for data, MLflow for experiments, Kubeflow for pipelines, and CI/CD for deployment.

Key Decisions

✓ DVC for data versioning (S3 backend)
✓ Kubeflow Pipelines for reproducibility
✓ Automated compliance logging for healthcare regulations

KubeflowMLflowDVCGitHub ActionsAWS SageMaker

Implementation

Phased rollout: data versioning first, then experiment tracking, finally automated pipelines.

Phase 1: Phase 1: Data Versioning
DVC tracking all training datasets with S3 backend.
Phase 2: Phase 2: Experiment Tracking
MLflow server logging all model training runs.
Phase 3: Phase 3: Automated Pipelines
Kubeflow running end-to-end retraining on data updates.

Technical Challenges

HIPAA compliance for model artifacts

Impact: Cannot store patient data in standard MLflow

Resolution: Encrypted artifact store with audit logging and access controls

Data versioning for large medical images

Impact: 10TB dataset causing DVC performance issues

Resolution: Sharded DVC storage with lazy downloading

Results

Model development lifecycle: Before6 months
After3 weeks
Improvement88% reduction
Reproducible experiments: Before0%
After100%
ImprovementFull reproducibility
Models in production: Before0
After5
Improvement5 new clinical models

Lessons Learned

📘 Data versioning was the hardest but most impactful piece
📘 Scientists adopted MLflow quickly when integrated with notebooks
📘 Automated compliance logging passed 3 regulatory audits

What We Would Do Differently

💡 Implement model monitoring from day one
💡 Use Feast for feature store earlier

Role Relevance

ML engineers built the platform that transformed research into production, reducing 6-month cycles to 3 weeks and enabling clinical deployment.

Critical Skills Demonstrated

MLOps platform designData versioning (DVC)Kubeflow pipelinesCompliance & audit logging

Frequently Asked Questions

How do you handle regulatory compliance?: Encrypted artifact storage, audit logs for all model accesses, and immutable experiment records.
What was the platform cost?: $150k/year for 10 data scientists, replacing 6 months of manual work per model.