Models degrade as data drifts. Our MLOps services automate the full model lifecycle, from versioned training and CI/CD deployment to drift monitoring and scheduled retraining, so your AI remains accurate, auditable and easy to update.
Maintain, retrain, and deploy AI models reliably at scale. At Prominent AI Services LLP, we deliver MLOps services end to end, from discovery and data preparation to deployment, monitoring and support, so your teams get dependable results quickly and securely.
Maintain, retrain, and deploy AI models reliably at scale.
Ideal for: Enterprises, AI Product Companies. We assess your current data flows and model operations, design an integration and MLOps architecture, automate deployment pipelines and set up monitoring dashboards and alerts for data and model health.
Common questions about our MLOps & Model Lifecycle Management services.
MLflow, Azure ML, SageMaker, Vertex AI, Kubeflow, GitHub Actions and Azure DevOps, adapted to your platform.
We monitor input data distributions and prediction quality against baselines and alert when thresholds are crossed.
Yes. Pipelines can retrain on new data, evaluate against the current model and promote only when performance improves.
