Edge AI / model serving · API / SDK

Seldon Core

Seldon

Deploy and observe machine-learning inference services.

Official documentation Download development spec

SELDON CORE 2 / DEVELOPER FIELD GUIDE

Choose an interface. Define a verifiable result.

Company or project context, ten technical entry points and a practical analysis of every reference.

2026-10-08 · Integration not tested

Company / project overview

Original summary of official company or project statements; use the source for the full original page.

Seldon Core 2 · About
Project	Seldon Core 2 manages model serving and dataflow pipelines through modular services.
Model servers	Its documented server integrations include MLServer and Triton.
Version boundary	This guide uses Core 2 documentation; Core 1 and separately listed enterprise modules require separate evaluation.

Complete official About page ↗

About reference — TA analysis
Audience	Buyers, partners and product owners shortlisting an AI provider.
Our assessment	Use positioning and company history for initial fit. Obtain project-specific deployment, support and commercial terms separately.

Official sources reviewed. No provider API or inference was run; results are illustrative internal contracts.

Top 10 technical entry points

Ten documented technical entry points selected for implementation decisions, not ten independent SDKs or an official ranking.

Choose by the work you need to complete
Interface	Purpose and audience
01 · InstallationAPI / SDK / developer guide	Choose learning or production installation.Platform operators
02 · Kubernetes resourcesAPI / SDK / developer guide	Declare runtime, servers, models and pipelines.Automation developers
03 · Inference serversAPI / SDK / developer guide	Match model requirements to server capabilities.Serving engineers
04 · Model resourcesAPI / SDK / developer guide	Declare model artifacts and runtime requirements.Model release teams
05 · ArchitectureAPI / SDK / developer guide	Separate management from inference data flow.System architects
06 · Inference server protocolAPI / SDK / developer guide	Submit REST, gRPC or Kafka inference.Client developers
07 · PipelinesAPI / SDK / developer guide	Chain model steps with tensor mappings.Dataflow developers
08 · AutoscalingAPI / SDK / developer guide	Choose model or server scaling strategy.Capacity engineers
09 · Operational metricsAPI / SDK / developer guide	Monitor requests, latency and serving resources.Operations teams
10 · Secure model endpointsAPI / SDK / developer guide	Configure authentication, authorization and TLS at ingress.Platform security engineers

Reference analysis

Capabilities summarize official documentation. Constraints and proposed tests are our engineering assessment.

API / SDK / developer guide

Installation

Installation · TA
Target audience	Platform operators
Documented capability	Choose learning or production installation.
Input → output	Environment and dependencies → deployed Core 2.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Kafka is required for dataflow pipelines, not every simple model-serving path.
Our suggested validation	Check dependencies for chosen features and release.

Official reference · Installation ↗

API / SDK / developer guide

Kubernetes resources

Kubernetes resources · TA
Target audience	Automation developers
Documented capability	Declare runtime, servers, models and pipelines.
Input → output	Resource manifests → managed serving resources.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Core 1 and Core 2 resources are not interchangeable.
Our suggested validation	Validate against installed CRD schema.

Official reference · Kubernetes resources ↗

API / SDK / developer guide

Inference servers

Inference servers · TA
Target audience	Serving engineers
Documented capability	Match model requirements to server capabilities.
Input → output	Model requirements and server capabilities → scheduling choice.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Capability labels must reflect actual installed dependencies.
Our suggested validation	Test unsupported requirements and insufficient memory.

Official reference · Inference servers ↗

API / SDK / developer guide

Model resources

Model resources · TA
Target audience	Model release teams
Documented capability	Declare model artifacts and runtime requirements.
Input → output	Model declaration → loading and serving status.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Submission is not evidence of readiness.
Our suggested validation	Inspect loaded version and readiness.

Official reference · Model resources ↗

API / SDK / developer guide

Architecture

Architecture · TA
Target audience	System architects
Documented capability	Separate management from inference data flow.
Input → output	Control changes and inference requests → distinct service paths.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Control-plane health and data-plane health are distinct.
Our suggested validation	Test each path during component failure.

Official reference · Architecture ↗

API / SDK / developer guide

Inference server protocol

Inference server protocol · TA
Target audience	Client developers
Documented capability	Submit REST, gRPC or Kafka inference.
Input → output	Protocol-compatible tensors → model outputs.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	REST JSON differs from Protobuf payloads required by gRPC/Kafka.
Our suggested validation	Validate transport-specific serialization and routing metadata.

Official reference · Inference server protocol ↗

API / SDK / developer guide

Pipelines

Pipelines · TA
Target audience	Dataflow developers
Documented capability	Chain model steps with tensor mappings.
Input → output	Pipeline input → combined model outputs.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Joins and dependency readiness affect completion.
Our suggested validation	Test missing branch data and correlation IDs.

Official reference · Pipelines ↗

API / SDK / developer guide

Autoscaling

Autoscaling · TA
Target audience	Capacity engineers
Documented capability	Choose model or server scaling strategy.
Input → output	Load policy → model/server replica changes.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Scaling models and scaling servers are different controls.
Our suggested validation	Check bottlenecks and minimum availability.

Official reference · Autoscaling ↗

API / SDK / developer guide

Operational metrics

Operational metrics · TA
Target audience	Operations teams
Documented capability	Monitor requests, latency and serving resources.
Input → output	Serving activity → monitoring data.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Operational health does not prove model quality.
Our suggested validation	Correlate metrics with model and pipeline versions.

Official reference · Operational metrics ↗

API / SDK / developer guide

Secure model endpoints

Secure model endpoints · TA
Target audience	Platform security engineers
Documented capability	Configure authentication, authorization and TLS at ingress.
Input → output	Access policy and request → allowed or denied route.
Execution environment	Version-pinned Core 2 deployment with selected server and infrastructure
Our decision constraint	Example ingress settings must match the deployed service mesh.
Our suggested validation	Test authorized and denied calls without exposing credentials.

Official reference · Secure model endpoints ↗

Official documentation

Official documentation · Supporting reference analysis
TA	Developers and technical product owners
Our validation advice	Pin the release and follow the target-specific support requirements before implementation.

Official reference · Official documentation ↗

04 / INPUT · EXPECTED OUTPUT · ACCEPTANCE

Expected results before implementation

Illustrative Core 2 application wrapper, not the literal Open Inference Protocol response. No cluster or model request was run.

Illustrative contract · no API call or model execution · not measured provider output

Loaded model inference

Expected behavior — illustrative, not executed
Input	Valid tensor request routed to a ready compatible model.
Expected result	ok with model/version and output tensors.
Acceptance	Verify response shape and model identity.

Empty application result

Expected behavior — illustrative, not executed
Input	Valid model inference produces no domain matches.
Expected result	ok with empty matches after application mapping.
Acceptance	Distinguish from incomplete pipeline join.

Pipeline waiting

Expected behavior — illustrative, not executed
Input	Required branch or correlated record is absent.
Expected result	pending until completion policy or timeout.
Acceptance	Do not present partial joins as complete predictions.

Access denied

Expected behavior — illustrative, not executed
Input	Ingress policy rejects the caller.
Expected result	error/denied without prediction data.
Acceptance	Preserve denial and redact credentials in diagnostics.

Map the expected result to your application

Map your actual runtime response into this proposed application contract. All values are invented examples; no credentials or live execution are included.

Expected output JSON — illustrative, not measured · SeldonCore-expected-results.json

{
  "schema_version": "1.0.0",
  "example": true,
  "provider": "Seldon Core 2",
  "status": "ok",
  "input_id": "sample-001",
  "data": {
    "model": "example-model",
    "version": "1",
    "request_id": "example-request",
    "outputs": [
      {
        "name": "prediction",
        "shape": [
          1,
          1
        ],
        "datatype": "INT64",
        "values": [
          [
            1
          ]
        ]
      }
    ]
  },
  "error": null,
  "verification": "illustrative-only;not-provider-response"
}

Download specification, example and acceptance plan

Engineering handoff — our proposed contract and gates
State semantics	Valid empty results differ from errors. Pending work must not appear successful before completion.
Evidence	Record model hash, runtime version, input contract and actual hardware. No accuracy or speed was measured here.
Acceptance proposal	Agree numerical tolerances and workload targets before testing on representative authorized samples.

Development assessment

Managed model rollout

Development concept · integration not tested

Provider capabilities above are based on official documentation or repositories. The proposed product, inputs, deliverable and acceptance criteria below are our development assessment.

Integration pilot

Validate runtime, schemas or hardware together before estimating a deployable product.

Proposed inputs: One pinned model, representative inputs, target runtime and workload.
Proposed deliverable: Predictions or deployment evidence with model version, latency and resource measurements.
Acceptance criterion: Verify outputs against a known sample and measure latency, memory and failed calls on the chosen target.
Dependencies: Model rights, supported operators, memory budget and hardware or serving infrastructure.

Development sequence

Confirm access to Seldon Core, license and the exact supported version.
Prepare the sample above and implement one documented operation for “Managed model rollout”.
Normalize the result with source, time and explicit error state; keep the provider response for review.
Run the acceptance criterion before estimating rollout effort or committing a customer deliverable.

How to validate demand

Record product views, documentation clicks, specification downloads and contextual hub clicks. These are event counts, not unique people or completed integrations.

This feasibility assessment uses implementation conditions. No traffic-based rank or delivery-time promise is assigned.

Seldon Core

Choose an interface. Define a verifiable result.

Company / project overview

Top 10 technical entry points

Reference analysis

Official documentation

Expected results before implementation

Loaded model inference

Empty application result

Pipeline waiting

Access denied

Map the expected result to your application

Managed model rollout

Development sequence

How to validate demand

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