MLOps Services
MLOps Services
OpsVigilant applies DevOps practices to machine learning workflows, enabling production-ready model deployments and repeatable ML pipelines. Our MLOps approach improves the reliability and lifecycle management of models while ensuring governance and reproducibility.
End-to-End ML Lifecycle Management
Manage model development through to production, including data validation, training, testing and deployment orchestration.
CI/CD for ML Models
Automate model testing, versioning and deployment so improvements reach production safely and consistently.
Model Monitoring & Drift Detection
Continuously monitor model accuracy and feature distributions to detect drift and trigger retraining workflows.
Containerized ML Workflows
Deploy models in portable containers using Kubeflow, Seldon or similar frameworks for scalable serving.
Governance & Reproducibility
Track datasets, model versions and experiments with audit-ready records for compliance and traceability.