### Other (Cleanup or Flake)

- Build etcd image v3.5.12 ([#123069](https://github.com/kubernetes/kubernetes/pull/123069), [@bzsuni](https://github.com/bzsuni)) [SIG API Machinery and Etcd]

## Dependencies

### Added
_Nothing has changed._

- 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]

- 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]

## 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

- 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]

  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]

### 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.

Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).

## Que signifie le déploiement

**Déployer** une application signifie effectuer les étapes nécessaires pour la rendre **disponible pour les
utilisateurs**.

Pour une **API Web**, cela implique normalement de la placer sur une **machine distante**, avec un **programme serveur**
qui offre de bonnes performances, une bonne stabilité, _etc._, afin que vos **utilisateurs** puissent **accéder** à
l'application efficacement et sans interruption ni problème.

- Kube-apiserver: fixes a potential crash serving CustomResourceDefinitions that combine an invalid schema and CEL validation rules. ([#126167](https://github.com/kubernetes/kubernetes/pull/126167), [@cici37](https://github.com/cici37)) [SIG API Machinery and Testing]