- The `build-tag` flag is reintroduced to conversion-gen and defaulter-gen which allow users to inject custom build tag during code generation process. ([#128259](https://github.com/kubernetes/kubernetes/pull/128259), [@dinhxuanvu](https://github.com/dinhxuanvu)) [SIG API Machinery]

- 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]

- 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 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]

- Restore --verify-only function in code generation wrappers. ([#123261](https://github.com/kubernetes/kubernetes/pull/123261), [@skitt](https://github.com/skitt)) [SIG API Machinery]
- Sample-apiserver manifest example will have correct RBAC ([#123479](https://github.com/kubernetes/kubernetes/pull/123479), [@Jefftree](https://github.com/Jefftree)) [SIG API Machinery and Testing]

Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

### Server Memory

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

## 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

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