KServe

As explained in Distributed Machine Learning Patterns

KServe is a Kubernetes-based model serving platform that provides standardized resources and abstractions for deploying machine learning models.

KServe is a Kubernetes-based model serving platform for deploying machine learning models across arbitrary frameworks. It addresses production serving concerns through a standardized custom resource and high-abstraction interfaces. section 8.3

KServe packages operational behavior that model deployments commonly need:

• Autoscale serving workloads, including GPU-backed deployments and scale-to-zero behavior. section 8.3 section 9.5
• Handle networking, health checking, and model-server configuration through Kubernetes-native resources. section 8.3
• Support canary rollouts and replicated model servers for production deployment workflows. section 8.3 section 9.5

End-to-end serving workflow

A distributed TensorFlow pipeline can train a model in a Kubernetes cluster, then deploy either a single-instance or replicated model server through KServe. As request volume increases, KServe creates additional serving Pods. section 9.5

Figure 8.5 KServe positioning in the ecosystem

Use the ecosystem diagram to locate KServe among Kubeflow components and understand its role in the machine-learning lifecycle. section 8.3

How Distributed Machine Learning Patterns covers it

How deep it goes

Explains serving abstractions and operational features, but defers framework-specific server configuration and detailed deployment procedures.

Examples

Uses single-instance and replicated model servers, increasing request traffic, additional serving Pods, and a TensorFlow Fashion-MNIST workflow.

What it assumes

Assumes Kubernetes-based deployment and machine learning model-serving needs while abstracting framework-specific serving complexity.

What you can do after

Operate/configure model deployments with autoscaling, replication, health checking, networking, and rollout support.

How it connects

Connects Kubernetes custom resources with Kubeflow, distributed model training, autoscaling, health checking, networking, and canary rollouts.

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Also explained in

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  Designing Deep Learning Systems: A guide for software engineersKServe is an open-source platform that provides standardized interfaces and infrastructure for deploying and serving machine-learning models on Kubernetes.

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Related concepts

• Model Servingused by · A model-serving platform is software infrastructure that hosts trained models and exposes inference by accepting requests and returning model outputs.
• Kubernetesuses · Kubernetes is an open-source platform for orchestrating and automating the deployment, scaling, and management of containerized workloads across computing clusters.
• custom resource definitionproduces · A CustomResourceDefinition is a Kubernetes API object that defines a custom resource type and its schema.
• Knativeuses · Knative is an open-source, Kubernetes-based platform for deploying and operating serverless and event-driven workloads.
• BentoMLalternative to · BentoML is an open-source framework for packaging, deploying, and serving machine-learning models as production services or APIs.
• autoscalinguses · Autoscaling is the automatic adjustment of computing resource capacity or running instances in response to workload demand or measured system conditions.

This explainer was generated by Manning's knowledge graph from the text of Distributed Machine Learning Patterns. The section links point to the parts of the book it draws on.