Production'da nasıl oldugunu görmek için şu bölüme bakın [Deployment](deployment/index.md){.internal-link target=_blank}.

## `async` ve `await`

For any non-trivial change, we need to be able to answer these questions:

* Why is this change done? What's the use case?
* For user facing features, what will the API look like?
* What test cases should it have? What could go wrong?
* How will it roughly be implemented? We'll happily provide code pointers to save you time.

</p>

<p>
Reads of memory locations larger than a single machine word
are encouraged but not required to meet the same semantics
as word-sized memory locations,
observing a single allowed write <i>w</i>.
For performance reasons,
implementations may instead treat larger operations
as a set of individual machine-word-sized operations
in an unspecified order.

### 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. 🚨

Articles:
  English:
  - author: Kurtis Pykes - NVIDIA
    link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/
    title: Building a Machine Learning Microservice with FastAPI
  - author: Ravgeet Dhillon - Twilio
    link: https://www.twilio.com/en-us/blog/booking-appointments-twilio-notion-fastapi
    title: Booking Appointments with Twilio, Notion, and FastAPI
  - author: Abhinav Tripathi - Microsoft Blogs

* **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.

* **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.

* 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**: 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.

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).