## Caso de uso

Vamos iniciar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso.

Vamos imaginar que você tem alguns **modelos de _machine learning_** que deseja usar para lidar com as requisições. 🤖

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande folha de papel com números e multiplicando todos eles juntos e ao mesmo tempo.

When referring to the remote machine, it's common to call it **server**, but also **machine**, **VM** (virtual machine), **node**. Those all refer to some type of remote machine, normally running Linux, where you run programs.

## Install the Server Program

When you install FastAPI, it comes with a production server, Uvicorn, and you can start it with the `fastapi run` command.

But you can also install an ASGI server manually:

=== "Uvicorn"

  // This field cannot be empty for new Events and it can have at most 128 characters.
  // +optional
  optional string reportingInstance = 5;

  // action is what action was taken/failed regarding to the regarding object. It is machine-readable.
  // This field can have at most 128 characters.
  // +optional
  optional string action = 6;

  // reason is why the action was taken. It is human-readable.
  // This field can have at most 128 characters.

- [ ] Security
- [ ] Test and Release
- [ ] User Experience
- [ ] Developer Infrastructure

**Please check any characteristics that apply to this pull request.**

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos al mismo tiempo.

pkg debug/pe, const IMAGE_DLLCHARACTERISTICS_WDM_DRIVER = 8192
pkg debug/pe, const IMAGE_DLLCHARACTERISTICS_WDM_DRIVER ideal-int
pkg debug/pe, const IMAGE_FILE_32BIT_MACHINE = 256
pkg debug/pe, const IMAGE_FILE_32BIT_MACHINE ideal-int
pkg debug/pe, const IMAGE_FILE_AGGRESIVE_WS_TRIM = 16
pkg debug/pe, const IMAGE_FILE_AGGRESIVE_WS_TRIM ideal-int
pkg debug/pe, const IMAGE_FILE_BYTES_REVERSED_HI = 32768

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).

    * **Intuitif** : Excellent support des éditeurs. <abbr title="aussi appelée auto-complétion, autocomplétion, IntelliSense...">Complétion</abbr> partout. Moins de temps passé à déboguer.
    * **Facile** : Fait pour être facile à utiliser et apprendre. Moins de temps passé à lire de la documentation.
    * **Concis** : Minimise la duplication de code. Plusieurs fonctionnalités à chaque déclaration de paramètre.