Seldon

Integrate Seldon Core's powerful model serving capabilities into your ZenML pipelines for seamless deployment of ML models to Kubernetes. This integration enables advanced deployment strategies, model explainability, outlier detection, and efficient management of complex ML workflows in production environments.

Features with ZenML

• Seamless Model Deployment to Kubernetes
  ‍Effortlessly deploy your ZenML pipeline models to Seldon Core on Kubernetes for production-grade serving.
• Advanced Deployment Strategies
  ‍Leverage Seldon Core's advanced deployment features like A/B testing, canary releases, and multi-armed bandits within ZenML pipelines.
• Streamlined Model Monitoring
  ‍Monitor your deployed models' performance, detect outliers, and explain predictions, all integrated with ZenML's tracking capabilities.
• Customizable Inference Servers
  ‍Deploy custom model serving logic using pre-built inference servers for popular ML frameworks or bring your own custom code.

Main Features

• Microservice-based architecture for model serving
• Built-in model explainability and outlier detection
• Advanced deployment strategies (A/B testing, canary releases, etc.)
• REST and gRPC inference endpoints
• Integration with Kubernetes native tools like Istio and Prometheus

How to use ZenML with Seldon

from zenml.integrations.seldon.steps import seldon_model_deployer_step
from zenml.integrations.seldon.services import SeldonDeploymentConfig
from zenml import pipeline

@pipeline
def seldon_deployment_pipeline():
    model = ...
    seldon_model_deployer_step(
        model=model,
        service_config=SeldonDeploymentConfig(
            model_name="my-model",
            replicas=1,
            implementation="SKLEARN_SERVER",
            resources=SeldonResourceRequirements(
                requests={"cpu": "100m", "memory": "100Mi"},
                limits={"cpu": "1", "memory": "1Gi"}
            )
        ),
    )

Additional Resources

• Seldon Core documentation