MLOps & Model Operations
Model registry, drift monitoring, and one-click rollback that keep already-deployed models reliable months after launch, not just on launch day.
- Timeline
- 3-5 weeks (new build), 4-6 weeks (retrofit to existing models)
- Team
- ML Engineer · Platform Engineer · DevOps Lead · QA Engineer
- Typical stack
- MLflow, Weights & Biases, or custom registry; cloud/on-prem; CI/CD integration (GitHub Actions, GitLab CI)
What you get
- Model registry with versioning, lineage tracking, and metadata tagging (MLflow/W&B/custom)
- Drift monitoring dashboard with statistical tests (KL divergence, PSI, data quality)
- Automated rollback workflow with last-known-good fallback and rollback criteria
- Evaluation harness with precision/recall/F1 benchmarks and confusion matrices
- CI/CD integration for model deployment with GitHub Actions/GitLab CI pipelines
- MLOps runbook with troubleshooting, scaling guidelines, and cost guardrails
Outcomes
- Registry & lineage in place
- Drift/accuracy monitoring with alerts
- Standardized deploy/rollback flows
- Measurable reduction in model deployment time
Selected work
$14.7M in fraud caught. False positives ↓85%.
TensorFlow anomaly detection across 12M receipts for a Fortune 200 financial services company. Fraud caught rose $2.1M → $14.7M/yr; false positives 28% → 4.2%; 60K audit hours cut to 8.4K. Payback in 2.8 months.
TensorFlow · Kafka · Rails · Textract
$18M annual fuel savings. 450 vessels.
PyTorch GNN re-optimizes routes every 6 hours across Atlantic, Pacific, and Indian Ocean lanes (previously quarterly, by hand). ETA accuracy ±5 days → ±12 hours; port congestion delays 14% → 3.2%. Still running 6 years later.
PyTorch · Rails · SageMaker · SAP
How we approach it
Centralized MLOps
- When:
- Single ML team, consistent tooling requirements, centralized governance needed
- Tradeoffs:
- Strong governance and consistency, but may slow down autonomous teams. Best for orgs prioritizing compliance.
- Best for:
- Finance, Healthcare, Regulated Industries
Federated MLOps
- When:
- Multiple ML teams, poly-cloud deployment, team autonomy prioritized
- Tradeoffs:
- Teams move faster with their preferred tools, but governance becomes harder. Requires strong platform team.
- Best for:
- Large enterprises, Multi-cloud, Product-driven orgs
Hybrid MLOps
- When:
- Balance of governance and flexibility needed, phased adoption
- Tradeoffs:
- Centralize critical governance (registry, monitoring) while allowing tool flexibility. Moderate complexity.
- Best for:
- Mid-market, Growing ML teams, Compliance-aware
Where teams use it
Finance
Fraud detection models with strict audit trails
Real-time fraud scoring models with complete lineage tracking, bias monitoring for fair lending compliance, and automated rollback on accuracy degradation below 94%
Healthcare
Clinical prediction models with HIPAA compliance
Patient risk stratification models with explainability requirements, PHI-safe model artifacts, audit logs for all predictions, and regulatory-compliant model versioning
Manufacturing
Predictive maintenance with edge deployment
Equipment failure prediction models deployed to edge devices with drift monitoring for sensor data, automated retraining triggers, and zero-downtime model updates
What we need from you
- Existing model artifacts and training code
- Deployment environments and CI/CD setup
- Accuracy thresholds and alert policies
- Rollback criteria
- Budget/cost guardrails
Proof points
- Model lineage graph
- Drift/accuracy monitoring dashboard
- Deployment time before/after
- Rollback test evidence
Built for procurement
- Model lineage and audit trail: Version tracking, dataset provenance, training metadata, deployment history with timestamps
- Drift detection SLAs: Real-time monitoring, alert escalation paths, automated rollback thresholds, incident response time commitments
- Rollback criteria: Performance degradation thresholds, last-known-good version identification, rollback success rate guarantees
- Bias monitoring and fairness: Demographic parity checks, equal opportunity metrics, disparate impact analysis for regulated models
- Cost monitoring and guardrails: Training cost budgets, inference cost tracking, automated scale-down policies, cost anomaly alerts
- Compliance evidence: SOC 2 Type II attestation, HIPAA compliance for healthcare models, Model risk management (SR 11-7) documentation
- Access control and authentication: RBAC for model registry, SSO integration, audit logging for sensitive model access