What We Deliver
End-to-end MLOps — from experiment to governed production deployment.
ML Training Pipelines
Automated feature prep, training, validation, and scheduling on AWS SageMaker, Azure ML, or Kubeflow.
Model Registry & Versioning
Track experiments, promote models through stages (dev/staging/prod), and maintain audit history.
Kubernetes Model Serving
Deploy models on K8s, KServe, or GPU node pools with autoscaling, canary releases, and A/B testing.
Monitoring & Drift Detection
Track latency, throughput, data drift, and model performance with alerts tied to your on-call stack.
CI/CD for ML
GitOps-style ML pipelines — test, build, deploy models through the same rigor as application code.
Governance & Compliance
Access controls, lineage tracking, and documentation for regulated industries and internal audit teams.
Technology Stack
Platforms: Kubernetes, AWS, Azure, GCP
MLOps tools: Kubeflow, MLflow, SageMaker, Azure ML, Argo Workflows
Observability: Prometheus, Grafana, OpenTelemetry, custom model metrics
Integration: Meuwic DevOps, AIOps, and GenAI practices for unified intelligent operations