Seldon Deploy provides oversight and governance for machine learning deployments. Easily deploy your models in an audited way with GitOps. Leverage advanced monitoring and perform alibi-powered explanations on requests.
Deploy machine learning models easily
Delays, bottlenecks, and months of work shouldn’t be the norm when DevOps and data scientists collaborate to get models into production. Deploy your machine learning models easily using industry-leading open-source projects like Seldon Core, regardless of model framework or library.
Ensure safe model deployment
Simplify the process of testing, monitoring and deploying models in live environments through intuitive dashboards and greater collaboration between data scientists and DevOps teams. Easily deploy your models in an audited way with GitOps. Leverage advanced monitoring to ensure your ML pipeline is robust and repeatable.
Audit model predictions using Black Box Model Explainers
Even when you're harnessing 'black box' Deep Learning models, Seldon Deploy will perform alibi-powered explanations on requests. 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.
Monitor running models and search request/response logs
Metrics and dashboards can monitor models to improve performance and rapidly communicate errors for easy debugging.
Update models via Canary workflows
Front-end deployment of models, explainers and canaries mean non-Kubernetes experts can deploy ML models and your testing can be done in live environments.
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