title: Deploy FastAPI on Azure App Service
  - author: Davide Fiocco
    author_link: https://github.com/davidefiocco
    link: https://davidefiocco.github.io/streamlit-fastapi-ml-serving/
    title: Machine learning model serving in Python using FastAPI and streamlit
  - author: Netflix
    author_link: https://netflixtechblog.com/
    link: https://netflixtechblog.com/introducing-dispatch-da4b8a2a8072

This means that you can send only the data that you want to update, leaving the rest intact.

!!! note
    `PATCH` is less commonly used and known than `PUT`.

    And many teams use only `PUT`, even for partial updates.

!!! tip
    If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

### Declare a *path parameter*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

!!! tip "Astuce"
    Pour ceux qui se demandent, "AlexNet", "ResNet", et "LeNet" sont juste des noms de <abbr title="Techniquement, des architectures de modèles">modèles</abbr> de Machine Learning.

### Déclarer un paramètre de chemin

Créez ensuite un *paramètre de chemin* avec une annotation de type désignant l'énumération créée précédemment (`ModelName`) :

```Python hl_lines="16"

## Add extra information

You can declare extra information in `Field`, `Query`, `Body`, etc. And it will be included in the generated JSON Schema.

You will learn more about adding extra information later in the docs, when learning to declare examples.

!!! warning
    Extra keys passed to `Field` will also be present in the resulting OpenAPI schema for your application.

Voici un petit bout de cette histoire.

## Alternatives

Je crée des API avec des exigences complexes depuis plusieurs années (Machine Learning, systèmes distribués, jobs asynchrones, bases de données NoSQL, etc), en dirigeant plusieurs équipes de développeurs.

Dans ce cadre, j'ai dû étudier, tester et utiliser de nombreuses alternatives.

```Python hl_lines="18  21  23"
{!../../../docs_src/path_params/tutorial005.py!}
```
Вы отправите клиенту такой JSON-ответ:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Path-параметры, содержащие пути

Предположим, что есть *операция пути* с путем `/files/{file_path}`.

返回给客户端之前，要把枚举元素转换为对应的值（本例中为字符串）：

```Python hl_lines="18  21  23"
{!../../../docs_src/path_params/tutorial005.py!}
```

客户端中的 JSON 响应如下：

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## 包含路径的路径参数

假设*路径操作*的路径为 `/files/{file_path}`。

但需要 `file_path` 中也包含*路径*，比如，`home/johndoe/myfile.txt`。

此时，该文件的 URL 是这样的：`/files/home/johndoe/myfile.txt`。

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

Here's a little bit of that history.

## Alternatives

I have been creating APIs with complex requirements for several years (Machine Learning, distributed systems, asynchronous jobs, NoSQL databases, etc), leading several teams of developers.

As part of that, I needed to investigate, test and use many alternatives.

The history of **FastAPI** is in great part the history of its predecessors.