MLOps & Platform Engineering
Automate model retraining, version control, canary deployments, drift monitoring, and horizontal host scaling.
Service Overview
MLOps ensures that models deployed to production remain accurate, stable, and cost-efficient over time. We build CI/CD pipelines for models, automate retraining triggers, deploy drift monitoring tools, configure canary release setups, and scale infrastructure. Our platform setups prevent model degradation and ensure compliance-by-design across all GPU compute allocations.
Interactive Simulator & Planner
Execute a simulated canary release pipeline to check continuous model integration logs.
Model Registration
Commit model binary to registry vault
Drift & Leakage Verification
Verify weights against validation set
Shadow Deployment
Route 10% of traffic as mirror queries
Health check & Rollout
Swap DNS records to active GPU containers
Key Capabilities & Features
CI/CD release pipelines for model updates
Automated drift detection and retraining triggers
Canary & Shadow deployment orchestration
Model performance monitoring (latency, memory, throughput)
GPU allocation optimization and autoscaling
Core Tech Stack
Key Deliverables
- MLflow server setups & configurations
- Docker/K8s deployment manifest files
- Prometheus alerting profiles and Grafana telemetry dashboards
Ready to deploy MLOps & Platform Engineering?
Consult with our senior AI architects to design a customized technical plan matching your corporate metrics.