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Readiness check

As explained in Machine Learning Platform Engineering

A periodic check that determines whether a workload is ready to receive traffic.

A readiness check periodically calls the /readyz endpoint to determine whether a Kubernetes pod is ready to receive traffic. Readiness requires the pod to be alive and operational enough to handle incoming requests. section 10.4.1

Readiness checks belong to the observability and pod-management layer of a deployed service. They complement liveness probes, which call /healthz to determine whether Kubernetes should restart a faulty process. section 10.4.1

BentoML service on Kubernetes

A BentoML Service includes built-in support for health checks and liveness/readiness probes, so a model-serving pod exposes the endpoints needed for Kubernetes monitoring without requiring custom endpoint implementation. section 10.4.1

Figure 10.7 BentoML’s built-in observability endpoints for K8s environments
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AI-generated content may be incorrect.

Inspect the built-in observability endpoints used by BentoML Services in Kubernetes environments. section 10.4.1

How Machine Learning Platform Engineering covers it

How deep it goes
Defines periodic /readyz polling and operational readiness, while deferring custom endpoint implementation.
Examples
Uses the /readyz endpoint and BentoML’s built-in Kubernetes observability endpoints as concrete examples.
What it assumes
Assumes familiarity with deployed services and Kubernetes pods while distinguishing readiness from liveness from the operational context.
What you can do after
Operate/configure BentoML services with Kubernetes readiness probes for traffic eligibility and pod observability.
How it connects
Connects observability endpoints with Kubernetes pods, health checks, liveness probes, and BentoML Services.

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