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Seldon Deploy - Machine Learning Drift and Outlier Detection
Create a machine learning deployment process with peak efficiency, minimal risk and the shortest time-to-value.
Get machine learning models to production faster, in the most reliable way.
Delays, bottlenecks and months of work shouldn’t be the norm when DevOps and data scientists collaborate to get models into production.
Simplify the process of testing, monitoring and deploying models in live environments through intuitive dashboards and greater collaboration between data scientists and DevOps teams.
You can read more about our product licenses, support services and SLA on our Solutions page.
Workflows
Front-end deployment of models, explainers and canaries means non-Kubernetes experts can deploy ML models and testing can be done in live environments.
Model management
Metrics and dashboards can monitor models to improve performance and rapidly communicate errors for easy debugging.
Model confidence
Model explainers mean you can understand and adjust what features are influencing the model and anomaly detection can flag drifts in data and alert users to adversarial attacks.
Stack stability
Backwards compatibility, rolling updates and full SLA alongside maintained integrations with all frameworks and clouds means a seamless install and reliable infrastructure.
“ With our MaaS platform running on Seldon, we’ve gone from it taking months to minutes to deploy or update models."
Steve Evangelista
Director of Product Management, Capital One
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