Seldon

Deploys ML models as containerized microservices on Kubernetes clusters, orchestrating multi-model inference pipelines through a declarative graph specification that defines routing, composition, and data flow between model endpoints. Uses Kubernetes Custom Resource Definitions (CRDs) to manage model lifecycle, enabling native integration with existing K8s infrastructure, service discovery, and resource management without requiring separate model serving infrastructure.

Constructs complex inference pipelines by composing multiple models into directed acyclic graphs (DAGs) with conditional branching, weighted routing, and data transformation between nodes. Supports request-time routing decisions based on input features, model confidence thresholds, or A/B test assignments, enabling sophisticated serving patterns like ensemble methods, model cascades, and contextual model selection without requiring application-level orchestration logic.

Manages multiple versions of the same model deployed simultaneously, enabling atomic switching between versions (blue-green deployments) with zero downtime. Supports versioning metadata (creation date, training data version, performance metrics) and enables rollback to previous versions if new versions degrade performance, with traffic routing controlled through Kubernetes service selectors or Istio virtual services.

Supports federated learning workflows where model updates are computed on distributed edge devices or data silos without centralizing raw data, with Seldon coordinating model aggregation and distribution. Enables privacy-preserving model training by keeping sensitive data local while updating global models through parameter aggregation, reducing data movement and regulatory compliance burden for sensitive data.

Implements traffic splitting strategies at the model serving layer, enabling gradual rollout of new model versions by routing a configurable percentage of requests to canary models while monitoring performance metrics. Supports multiple traffic splitting algorithms (percentage-based, header-based, cookie-based) and integrates with monitoring systems to automatically detect performance regressions, enabling safe model updates without application-level experiment frameworks.

Continuously monitors model predictions and input data distributions in production, detecting data drift (changes in input feature distributions), prediction drift (changes in model output distributions), and performance degradation through statistical tests and anomaly detection. Integrates with Prometheus metrics collection and Grafana dashboards, exposing drift metrics as time-series data that trigger alerts when thresholds are exceeded, enabling proactive model retraining decisions without manual monitoring.

Generates human-interpretable explanations for individual model predictions using multiple explanation methods (SHAP, LIME, anchor-based explanations) that identify which input features most influenced the prediction. Integrates explanation generation into the serving pipeline, returning feature importance scores and decision boundaries alongside predictions, enabling stakeholders to understand and audit model decisions for regulatory compliance or debugging.

Automatically logs all model predictions, input features, and serving decisions to persistent storage with timestamps and metadata, creating immutable audit trails for regulatory compliance and debugging. Supports configurable logging backends (Elasticsearch, S3, databases) and enables filtering/querying of prediction history by model version, time range, or feature values, facilitating root cause analysis and compliance audits without requiring application-level logging.

Provides a standardized interface for wrapping custom ML models (scikit-learn, TensorFlow, PyTorch, XGBoost, custom Python code) as Seldon-compatible inference servers that expose REST and gRPC endpoints. Supports multiple wrapper patterns (Python class-based, Docker container-based, language-agnostic) enabling models trained in any framework to be deployed without modification, with automatic request/response serialization and error handling.

Enables custom data transformation logic to execute within the serving pipeline, allowing feature engineering, input validation, and response formatting to occur at serving time without requiring application-level preprocessing. Supports transformer components that intercept requests/responses, apply custom Python logic, and modify data before passing to models, enabling dynamic feature engineering based on request context or real-time data sources.

Enables deployment of the same model serving infrastructure across multiple cloud providers (AWS, GCP, Azure) and on-premises Kubernetes clusters through cloud-agnostic containerization and Kubernetes abstraction. Models packaged as OCI containers can be deployed identically across environments without modification, with cloud-specific integrations (IAM, networking, storage) handled through Kubernetes-native mechanisms, enabling vendor lock-in avoidance and hybrid cloud strategies.

Automatically scales model serving replicas based on request load, latency, or custom metrics using Kubernetes Horizontal Pod Autoscaler (HPA) integration. Supports multiple scaling policies (CPU-based, memory-based, custom metrics from Prometheus) enabling efficient resource utilization and cost optimization, with configurable scaling thresholds and cooldown periods to prevent thrashing.