Seldon is an open ecosystem for deploying, scaling, and monitoring machine learning models on Kubernetes. Serve models as REST or gRPC with canaries, A/B tests, and shadow traffic. Request logs feed drift, fairness, and performance checks. Pipelines integrate explainability and guardrails. With GitOps, integrations, and dashboards, teams move from notebooks to reliable, observable production inference.
Package models as containers or inference graphs and deploy to clusters with REST or gRPC endpoints. Autoscaling reacts to traffic and resource limits. Canary and shadow releases de-risk changes before switching live. Routing policies handle A/B experiments. This native approach meets platform standards, letting ML teams reuse cluster security, logging, and networking while focusing effort on performance and reliability.
Collect request and prediction logs to watch latency, errors, and data skew. Drift detectors track feature shifts; slice metrics reveal underserved cohorts. Alerting connects to on-call tools. With evidence in dashboards, teams separate model issues from platform noise faster. Trend views inform retraining schedules and SLA budgets, while stored traces make incident reviews concrete instead of guesswork.
Integrate explainers such as SHAP or anchor methods to illuminate feature impact for decisions. Outlier detection and content guardrails flag risky inputs. Policy checks block unsafe outputs before they leave. By building explanations and safety into the inference path, organizations improve trust with auditors and customers while protecting brand and end users from unintended behavior in production systems.
Connect CI/CD and data workflows to automate build, validation, and rollout. GitOps tracks declarative changes for auditability. Feature checks and contract tests prevent mismatches between training and serving. With repeatable pipelines, teams shorten feedback loops and keep environments consistent. Automation reduces manual steps that cause incidents, turning model updates into routine operations across squads.
Hook into Prometheus, Grafana, OpenTelemetry, and ticketing tools so monitoring, tracing, and escalation follow existing playbooks. Role-based access and namespaces isolate teams. Policies define who can deploy and where. This governance creates a shared source of truth for models in production, aligning ML, platform, and compliance leaders while maintaining velocity on experiments and long-lived services.
Best for platform teams and data science groups running many models under uptime expectations. With Kubernetes-native serving, drift and performance monitoring, explainability, and GitOps, Seldon helps organizations ship reliable inference, detect risk early, and coordinate retrains while giving leaders traceability across model changes, incident timelines, and compliance reviews in complex environments.
Seldon replaces one-off scripts, fragile endpoints, and blind spots in production with governed, observable serving. Teams canary updates safely, watch drift and latency, and block unsafe outputs. Pipelines automate validation and rollout while integrations connect to existing monitoring and on-call. The result is faster iteration, fewer incidents, and trustworthy model behavior across products and regions.
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