### 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.

## 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

## 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.

## What Does Deployment Mean { #what-does-deployment-mean }

To **deploy** an application means to perform the necessary steps to make it **available to the users**.

For a **web API**, it normally involves putting it in a **remote machine**, with a **server program** that provides good performance, stability, etc, so that your **users** can **access** the application efficiently and without interruptions or problems.

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).

{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

## `fastapi run` { #fastapi-run }

Executing `fastapi run` starts FastAPI in production mode by default.

## Full Stack FastAPI MongoDB

... müsaitliğime ve diğer faktörlere bağlı olarak daha sonra gelebilir. 😅 🎉

## Machine Learning modelleri, spaCy ve FastAPI

GitHub: <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a>

### Machine Learning modelleri, spaCy ve FastAPI - Features

* **spaCy** NER model entegrasyonu.

#
#	GOOS=linux GOARCH=ppc64 bootstrap.bash
#
# this script cross-compiles a toolchain for that GOOS/GOARCH
# combination, leaving the resulting tree in ../../go-${GOOS}-${GOARCH}-bootstrap.
# That tree can be copied to a machine of the given target type
# and used as $GOROOT_BOOTSTRAP to bootstrap a local build.
#
# Only changes that have been committed to Git (at least locally,
# not necessary reviewed and submitted to master) are included in the tree.

researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)