Seldon

Deploys ML models as containerized microservices on Kubernetes clusters using a declarative YAML-based configuration model that abstracts framework differences (TensorFlow, PyTorch, scikit-learn, XGBoost, custom models). Models are wrapped in standardized serving containers that expose REST/gRPC endpoints, with automatic scaling, resource management, and service discovery handled by Kubernetes orchestration primitives.

Constructs directed acyclic graphs (DAGs) of model inference steps where requests flow through multiple models sequentially or in parallel, with conditional routing logic based on model outputs, feature engineering steps, or external data lookups. Routing decisions are evaluated at runtime using a graph execution engine that optimizes for latency and resource utilization across the DAG.

Implements online learning algorithms (epsilon-greedy, Thompson sampling, UCB) that dynamically select between multiple models based on observed rewards (user feedback, business metrics) from previous predictions. Bandit algorithms learn which model performs best for different request contexts and automatically route traffic to higher-performing models, enabling continuous optimization without explicit A/B test design.

Supports training model updates on distributed data without centralizing raw data, using techniques like federated averaging where model updates are computed locally on edge devices or data silos and aggregated centrally. Privacy-preserving techniques (differential privacy, secure aggregation) can be applied to protect sensitive data during the aggregation process, enabling collaborative model improvement across organizations or data boundaries.

Gradually routes a percentage of production traffic to new model versions while monitoring performance metrics, with automatic rollback if error rates or latency exceed thresholds. Traffic splitting is implemented at the Kubernetes service mesh level (Istio/Linkerd integration) or via Seldon's built-in traffic router, allowing fine-grained control over which requests reach which model versions based on user segments, request features, or random sampling.

Continuously monitors input feature distributions and model prediction outputs against historical baselines, detecting statistical drift using methods like Kolmogorov-Smirnov tests or custom drift detectors. Metrics are collected from model inference requests, aggregated in a time-series database, and compared against configurable thresholds to trigger alerts when data or model performance degrades, enabling proactive retraining decisions.

Generates human-interpretable explanations for individual model predictions using multiple explanation methods (SHAP, LIME, anchors, integrated gradients) that highlight which input features most influenced the prediction. Explanations are computed on-demand or cached for frequently-seen inputs, and can be returned alongside predictions in the same API response, enabling end-users and stakeholders to understand model decisions.

Automatically logs all model predictions, input features, and decision metadata to a persistent audit store (Elasticsearch, cloud storage) with immutable records that include timestamps, model versions, user identifiers, and feature values. Audit logs can be queried for compliance investigations, model behavior analysis, and regulatory reporting, with built-in support for data retention policies and personally identifiable information (PII) redaction.

Manages multiple versions of the same model deployed simultaneously, allowing requests to be routed to specific versions or automatically selecting the latest version. Updates to model versions are performed using Kubernetes rolling updates that gradually replace old replicas with new ones while maintaining service availability, with automatic rollback to previous versions if health checks fail.

Automatically scales the number of model server replicas up or down based on CPU, memory, or custom metrics (request latency, queue depth) using Kubernetes Horizontal Pod Autoscaler (HPA). Scaling policies can be configured per-model with minimum/maximum replica counts, scale-up/down rates, and cooldown periods to prevent thrashing, optimizing resource utilization and cost while maintaining latency SLAs.

Provides a standardized interface (Python SDK or language-agnostic container contract) for wrapping custom model inference code, allowing models to be served without modification to the original code. Wrappers handle request/response serialization, batching, caching, and integration with Seldon's monitoring and explainability features, supporting any model format or custom inference logic written in any language.

Chains together transformation steps that preprocess incoming requests (feature engineering, normalization, encoding) and postprocess model outputs (decoding, formatting, aggregation) using a pipeline abstraction. Transformations can be implemented as custom Python code, SQL queries, or calls to external services, and are executed in the serving path with caching to avoid redundant computation.