H2O MLOps
The MLOps platform that deploys, governs, and monitors any model in production, with drift and fairness monitors, one-click rollback, and an audit log for every score.
- H2O MLOps manages, deploys, governs, and monitors models from H2O, PyTorch, scikit-learn, XGBoost, MLflow, and pickle.
- Drift, accuracy, and fairness monitors with thresholds that alert or trigger automatic retraining; explanations logged per scoring request.
- Champion/challenger and A/B deployments, one-click update and rollback, high availability to five nodes.
- Runs on your Kubernetes, fully managed, or hybrid, in PCI-DSS, HIPAA, and FedRAMP Certified environments.
Everything after the model is trained.
Training is a fraction of the lifecycle. MLOps is the rest of it, governed.
- RegistryRegister models from any framework with versions, metadata, and lineage back to the data and code that produced them.
- DeployReal-time or batch, single model, A/B, or champion/challenger, with one-click update and rollback on your Kubernetes, managed, or hybrid.
- MonitorDrift, accuracy, and fairness with thresholds that alert or trigger automatic retraining, catching issues before the business feels them.
- ExplainExplanations logged for every scoring request, so the reason code travels with each prediction wherever the model runs.
- AuditAudit logs for every deployment, promotion, and rollback: who changed what, when, and why, ready for review.
- ComplyRuns in PCI-DSS, HIPAA, and FedRAMP Certified environments, with high availability across up to five nodes.
From registered model to monitored endpoint.
- 01RegisterAny framework, and lineage is captured automatically from the H2O engines that produced the model.
- 02DeployChoose real-time or batch, single or champion/challenger. An endpoint in minutes, not a project.
- 03MonitorDrift, accuracy, and fairness. Set thresholds, and explanations are logged for every request.
- 04ActAlert the team, retrain automatically, or roll back in one click, all from one place.
Where the predictive lane meets production.
MLOps serves models to apps and APIs and monitors them in production. A deliberate boundary: MLOps does not deploy to h2oGPTe; Cascade serves language models through its own Model Zoo. Highlighted is MLOps.
What ships today
Every model an agent ships goes through here.
When the Data Science Agent finishes a model, MLOps is where it lands: registered, deployed with a challenger, monitored for drift. Agent-built and human-built models get the same governance, because they go through the same door.
You operate it, or H2O AI operates it for you.
Same platform either way: the same capabilities, the same governance, and the same audit trail. The choice stays open, so you can switch between them as the business changes, without rebuilding what runs on top.
H2O AI Cloud for Government
The H2O AI Cloud for Government has achieved FedRAMP High Certification, delivering the highest standard of cloud security so federal agencies can harness predictive and generative AI without compromising data sovereignty or compliance.
Questions people ask before they call us.
H2O (MOJO), PyTorch, scikit-learn, XGBoost, MLflow, and pickle. The registry is framework-agnostic; lineage is captured automatically from H2O engines.
Bring the model that is in production with no monitoring.
We will register it, deploy a challenger beside it, and switch on drift and fairness monitors before you leave.