Seldon Core: Kubernetes ML Deployment

Overview Seldon Core is an MLOps framework specifically designed to deploy machine learning models on Kubernetes. It converts your model into a production-ready microservice with metrics, logging, and scaling.

Key Features

1. Inference Graphs You can chain models together.

• Input -> [Preprocessing Model] -> [Classifier A] -> Output.
• Input -> [Router] -> (Model A or Model B) -> Output (A/B Testing).

2. GitOps Friendly You define your deployment as a Kubernetes YAML manifesto.

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  predictors:
  - graph:
      name: classifier
      implementation: SKLEARN_SERVER
      modelUri: s3://my-bucket/model

3. Standard Metrics Automatically exports request count, latency, and custom metrics to Prometheus/Grafana.

4. Explanations Native integration with Alibi (Explainable AI library) to explain why the model made a prediction.

Use Case Seldon is "Heavy Duty". Use it if you are already running Kubernetes and need to manage hundreds of models at scale in an enterprise environment.