- Fixed incorrect "v1 Binding is deprecated in v1.6+" warning in kube-scheduler log. ([#125540](https://github.com/kubernetes/kubernetes/pull/125540), [@pohly](https://github.com/pohly)) [SIG API Machinery]
- Fixed the comment for the Job's managedBy field. ([#124793](https://github.com/kubernetes/kubernetes/pull/124793), [@mimowo](https://github.com/mimowo)) [SIG API Machinery and Apps]

- Kube-apiserver: fixes a 1.27+ regression watching a single namespace via the deprecated /api/v1/watch/namespaces/$name endpoint where watch events were not delivered after the watch was established ([#126150](https://github.com/kubernetes/kubernetes/pull/126150), [@xyz-li](https://github.com/xyz-li)) [SIG API Machinery and Testing]

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

Imaginemos que tienes algunos **modelos de machine learning** que quieres usar para manejar requests. 🤖

		}
	}

	return host, port, nil
}

// isLocalHost - checks if the given parameter
// correspond to one of the local IP of the
// current machine
func isLocalHost(host string, port string, localPort string) (bool, error) {
	hostIPs, err := getHostIP(host)
	if err != nil {
		return false, err
	}

	nonInterIPV4s := mustGetLocalIP4().Intersection(hostIPs)

- Fixed an issue in register-gen where imports for k8s.io/apimachinery/pkg/runtime and k8s.io/apimachinery/pkg/runtime/schema were missing. ([#130392](https://github.com/kubernetes/kubernetes/pull/130392), [@mrIncompetent](https://github.com/mrIncompetent)) [SIG API Machinery]

- Fix counting error in service/nodeport/loadbalancer quota check ([#97826](https://github.com/kubernetes/kubernetes/pull/97826), [@pacoxu](https://github.com/pacoxu)) [SIG API Machinery and Network]
- Fix missing cadvisor machine metrics. ([#97006](https://github.com/kubernetes/kubernetes/pull/97006), [@lingsamuel](https://github.com/lingsamuel)) [SIG Node]

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

- Kube-apiserver: resolved a regression that treated `304 Not Modified` responses from aggregated API servers as internal errors ([#112530](https://github.com/kubernetes/kubernetes/pull/112530), [@liggitt](https://github.com/liggitt)) [SIG API Machinery]

  being written to the API audit log. (#94986, @tkashem) [SIG API Machinery, Auth, Cloud Provider and Testing]
- Create HPA v2 from v2beta2 with some fields changed. (#102534, @wangyysde) [SIG API Machinery, Apps, Auth, Autoscaling and Testing]