Ship Healthcare Software Faster. With Enterprise-Grade MLOps.
Zabrizon's DevOps and MLOps engineering team builds the CI/CD, infrastructure automation, and ML lifecycle management capabilities that allow healthcare organisations to deploy and iterate on AI systems safely, rapidly, and at scale.
DevOps & MLOps Engineering Services
Delivery pipelines and ML infrastructure designed for the compliance demands of healthcare.
CI/CD Pipeline & DevSecOps
End-to-end continuous integration and delivery pipelines with security scanning, compliance gates, and automated testing — enabling healthcare teams to deploy daily with confidence.
- GitHub Actions, GitLab CI, and Azure DevOps pipeline design
- SAST, DAST, and dependency scanning integrated into pipelines
- HIPAA and SOC 2 compliance gates in deployment workflows
- Blue/green and canary deployment strategies for zero downtime
Kubernetes & Container Orchestration
Production Kubernetes environments for healthcare workloads — EKS, AKS, and GKE with multi-tenancy, network policy, and secrets management configured for PHI environments.
- EKS, AKS, GKE cluster design and deployment
- Helm chart development and GitOps with ArgoCD / Flux
- Network policy and pod security for PHI isolation
- Horizontal pod autoscaling for clinical burst workloads
ML Model Lifecycle & MLOps Platform
End-to-end ML model management from training through production monitoring — feature stores, experiment tracking, model registry, and automated retraining pipelines.
- MLflow, SageMaker, and Vertex AI platform deployment
- Feature store development and feature engineering pipelines
- Model performance monitoring and data drift detection
- Automated retraining triggers based on drift thresholds
DevOps & MLOps Engagement Approach
Practical, embedded engineering — working alongside your team to build lasting capability.
Current State Assessment
DORA metrics baseline, delivery pipeline audit, and infrastructure review to identify bottlenecks and compliance gaps.
Platform Design & Toolchain Selection
Target DevOps/MLOps architecture design with toolchain selection aligned to your cloud platform and engineering skills.
Pipeline & Platform Build
Sprint-based implementation of CI/CD pipelines, container orchestration, and ML infrastructure — embedded with your engineering team.
Team Enablement & Docs
Runbook development, team training, and knowledge transfer ensuring your team can operate the new platform independently.
Ongoing Operations & Optimisation
Optional managed operations, SRE support, and continuous improvement retainer post-implementation.
Ready to Modernise Your Healthcare Engineering Delivery?
Our DevOps and MLOps engineers will assess your current delivery pipeline and show you where AI teams can move 10× faster.