How Sonrai used Amazon SageMaker and MLOps to speed biomarker discovery from months to minutes

• TL;DR
• Sonrai built an MLOps-first workflow on Amazon SageMaker that modeled 8,916 biomarkers across proteomics, metabolomics and lipidomics, ran hundreds of experiments, and produced 15 candidate models.
• The top model (proteomics + metabolomics) delivered 94% sensitivity, 89% specificity and an AUC-ROC of 0.93—meeting Sonrai’s regulatory thresholds for clinical validation.
• Full pipeline (raw data → models & stakeholder reports) runs in under 10 minutes, cutting curation time by ~50% and enabling daily iterations.
• Key enablers: Amazon S3 for secure data, MLflow for experiment tracking, Recursive Feature Elimination (RFE) for feature pruning, SageMaker Model Registry for governance, and planned automation via EventBridge + SageMaker Pipelines.

Background: the business problem and why it matters

Precision medicine promises earlier, personalized diagnosis, but the practical bottleneck is not ambition—it’s data. Omic datasets routinely contain thousands of candidate signals (features) but only a few hundred patient samples. That imbalance—commonly called the “curse of dimensionality”—is like having thousands of light switches but only a handful of rooms to test them in: most combinations will overfit and fail to generalize.

For life sciences companies and healthcare providers, the stakes are high. A noisy discovery process creates wasted experiments, delays to clinical validation, and regulatory risk. Sonrai partnered with AWS to flip the script: apply MLOps discipline and cloud-managed tooling so exploratory biomarker discovery becomes repeatable, auditable, and fast enough to be operationally useful.

Architecture & tools: the practical stack that made it happen

The architecture stitches together secure storage, reproducible pipelines, experiment provenance, and model governance:

• Amazon S3 — secure object storage for raw patient data, intermediate artifacts, and final reports with tiered access controls.
• Amazon SageMaker — used for training, hosting, and pipelines; SageMaker Model Registry provides lifecycle states and packaged inference artifacts for auditability.
• MLflow — served as the single source of truth for experiments: metrics, hyperparameters, artifacts, and full lineage.
• Recursive Feature Elimination (RFE) — applied iteratively to prune thousands of candidate biomarkers down to robust predictors (think of it like pruning a tree to reveal the strongest branches).
• Quarto — integrated into pipelines to produce stakeholder-ready, reproducible reports for scientists and regulators.
• EventBridge + SageMaker Pipelines — planned to automate retraining and response to data drift.

“Using SageMaker AI for the full model development process enabled the team to collaborate and rapidly iterate with full traceability and confidence in the final result. The rich set of services available in Amazon SageMaker AI make it a complete solution for robust model development, deployment, and monitoring.” — Matthew Lee, Director of AI & Medical Imaging at Sonrai

Why experiment tracking and a model registry matter

For regulated workflows, it’s not enough to produce an accurate model—you must show how it was produced. Sonrai ties raw S3 objects and Git commits to MLflow experiment runs, so every metric, hyperparameter change, and artifact has a timestamped provenance. When a candidate model passes gates, it’s promoted into the SageMaker Model Registry where lifecycle states (Pending, Approved, Rejected), packaged inference code, and metadata are stored for handoff to clinical validation teams.

Methods & validation: shrinking the feature space responsibly

Modeling 8,916 biomarkers across multiple modalities (proteomics, metabolomics, lipidomics) demands care to avoid false discoveries. Sonrai combined iterative RFE with disciplined experiment tracking to compare single‑modality and multi‑modal models while guarding against overfitting. Each RFE iteration was logged in MLflow so the team could reproduce and audit which features were dropped and why.

Model evaluation emphasized clinical utility, not just raw accuracy. Key metrics and thresholds used for approval were:

• Sensitivity ≥ 90%
• Specificity ≥ 85%
• AUC-ROC ≥ 0.90

A brief primer on those terms: sensitivity measures how many true positives the test catches (important for early detection), specificity measures how many true negatives it correctly identifies (important to avoid false alarms), and AUC-ROC is a summary of discrimination across thresholds (higher is better).

To reduce overfitting and validate robustness, the workflow combined cross-validation and held‑out evaluation strategies alongside the iterative RFE experiments. That approach allowed the team to surface models that generalized across folds and to provide a verifiable record that models weren’t tuned to noise.

Results: speed, accuracy, and governance

• Features modeled: 8,916 candidate biomarkers
• Experiments executed: hundreds, all tracked in MLflow
• Candidate models evaluated: 15 spanning single-modality and multi‑modal combinations
• Top model (proteomics + metabolomics): 94% sensitivity, 89% specificity, AUC-ROC of 0.93
• End-to-end time-to-iteration: pipeline ran raw data → models & reports in under 10 minutes
• Operational efficiency: ~50% reduction in time spent curating data for biomarker reports

Speed here is not a vanity metric. Running the full pipeline in minutes enabled daily updates, more hypotheses evaluated per week, and interactive collaboration between data scientists, clinicians, and validation teams. The SageMaker Model Registry and MLflow trail delivered the verifiable artifacts regulators expect: packaged inference code, versioned artifacts, and documented approval decisions.

Deployment options and cost trade-offs

Practical cost controls include using spot instances for training, right-sizing inference instances, choosing batch transforms for non-real-time workloads, and applying model compression or quantization where appropriate.

Risks & mitigations: what to watch and how to respond

Faster iterations and auditable pipelines reduce many risks, but several remain and deserve explicit mitigation:

• Small sample size and overfitting — Mitigation: nested cross-validation, held‑out cohorts, bootstrap uncertainty estimates, and external validation where possible.
• Batch effects and normalization — Mitigation: standardized preprocessing pipelines, careful QC, and logging of provenance so data shifts are traceable.
• Regulatory acceptance — Mitigation: package inference code and model metadata, document decision thresholds, and preserve audit trails for every promotion and rejection in the Model Registry.
• Federated learning governance — Mitigation: use secure aggregation, differential privacy or secure enclaves, and standardized legal frameworks for cross‑site collaborations.
• Cloud cost and vendor dependence — Mitigation: design exportable inference containers, keep compute-optimized architectures, and run cost reviews as part of the governance process.

Practical checks to validate reproducibility

• Versioned datasets in Amazon S3 with access controls and immutable snapshots.
• Git-linked code commits recorded with every MLflow experiment.
• Unit and integration tests for preprocessing, model interfaces, and scoring logic.
• Model Registry entries with packaged inference code, evaluation reports, and approval justifications.

Next steps and recommendations for executives

For organizations evaluating precision medicine AI, the technical lessons translate directly into business actions:

• Start with data governance and provenance. If you can’t trace the exact dataset used to train a model, regulatory validation becomes an uphill battle.
• Adopt experiment tracking early. MLflow (or equivalent) pays dividends by making decisions reproducible and defensible.
• Prioritize feature-selection discipline. RFE and iterative pruning reduce false discoveries and spotlight robust biology.
• Plan for automated retraining and drift monitoring. Event-driven retraining with SageMaker Pipelines keeps models clinically relevant.
• Design federated collaborations carefully. The privacy gains are real, but operational, legal, and cryptographic trade-offs require upfront planning.

Recommended executive checklist before a pilot:

• Data readiness: labeled cohorts, metadata, and S3 access controls — yes/no
• Compliance readiness: HIPAA alignment, IRB approvals, and legal contracts — yes/no
• Technical readiness: MLOps tooling (experiment tracking, pipelines, registry) — yes/no
• Resourcing: cross-functional team with data science, clinical, and regulatory representation — yes/no
• Timeline: prototype in 8–12 weeks, validation in months depending on external cohorts

Questions & quick answers

• How can organizations manage extreme feature-to-sample ratios?
  

  Combine disciplined feature selection (RFE tracked per iteration), multi-modal modeling, robust cross-validation, and held-out evaluation so you can test many hypotheses without losing reproducibility.

• What does traceability look like for clinical-grade ML?
  

  End-to-end lineage: Amazon S3 raw objects + Git commits → MLflow experiments (metrics, hyperparameters, artifacts) → SageMaker Model Registry entries with lifecycle states, packaged inference code and recorded approvals.

• Can cloud-managed MLOps shorten iteration cycles in regulated workflows?
  

  Yes—the Sonrai pipeline runs raw data to models and reports in under 10 minutes, enabling daily iterations and a roughly 50% reduction in manual curation time.

• Do multi-modal omic models outperform single-modality models?
  

  In Sonrai’s work the combined proteomics + metabolomics model outperformed single modalities, reaching 94% sensitivity, 89% specificity, and an AUC-ROC of 0.93.

Final takeaway

The convergence of high-dimensional biology and mature cloud MLOps makes clinical-grade biomarker discovery practical at scale. Sonrai’s playbook—secure data in Amazon S3, disciplined experiment tracking with MLflow, iterative feature pruning with RFE, and formal model promotion via SageMaker Model Registry—turns exploratory signals into auditable, regulation-ready models rapidly. For executives, the choice is clear: invest in governance and MLOps first, and the time-to-value for precision medicine AI drops from months to minutes.

Ready to assess whether your organization is MLOps-ready for precision medicine? Start with a one-page readiness checklist: data provenance, compliance gating, experiment tracking, model lifecycle policies, and a cross-functional validation plan. These five items separate reproducible clinical assets from one-off findings.