Automate ML lifecycles.
End-to-end MLOps pipeline. Data validation, training, evaluation, deployment, and monitoring in a unified workflow.
End-to-end
Stages
CI/CD
Deploy
Drift detect
Monitor
Versioned
Registry
ML, automated.
End-to-end MLOps with drift detection.
Pipeline orchestration
DAG-based pipeline with data → train → eval → deploy stages.
CI/CD for ML
Automated retraining and deployment on data changes.
Data validation
Schema validation and data drift detection.
Model evaluation
Automated evaluation with custom metrics and thresholds.
A/B deployment
Canary and blue-green deployment with traffic splitting.
Model monitoring
Monitor prediction drift, latency, and data quality.
Getting started
Launch your first instance in three steps. CLI, console, or API — your choice.
ur ml pipeline create fraud-det \
--stages="validate,train,eval,deploy"MLOps patterns.
Production ML and regulated pipelines.
Suggested configuration
CI/CD · Drift · A/B deploy
Estimate your costs
Create detailed configurations to see exactly how much your architecture will cost. Pay for what you use, down to the second.
Configuration 1
MLOps Platform
Usage Volume
Infrastructure
Options
Cost details
End-to-end MLOps. Data validation to model monitoring.
Works seamlessly with
Frequently asked questions
ML, automated.
End-to-end MLOps with drift detection.