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docs/en/docs/async.md
The cashier goes to the kitchen. You wait, standing in front of the counter 🕙, so that no one else takes your burgers before you do, as there are no numbers for turns. <img src="/img/async/parallel-burgers/parallel-burgers-03.png" class="illustration"> As you and your crush are busy not letting anyone get in front of you and take your burgers whenever they arrive, you cannot pay attention to your crush. 😞
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docs/fr/docs/async.md
Mais vous pouvez aussi profiter du parallélisme et multiprocessing afin de gérer des charges **CPU bound** qui sont récurrentes dans les systèmes de *Machine Learning*. Ça, ajouté au fait que Python soit le langage le plus populaire pour la **Data Science**, le **Machine Learning** et surtout le **Deep Learning**, font de **FastAPI** un très bon choix pour les APIs et applications de **Data Science** / **Machine Learning**.
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docs/de/docs/async.md
Wenn Sie über gute technische Kenntnisse verfügen (Coroutinen, Threads, Blocking, usw.) und neugierig sind, wie FastAPI mit `async def`s im Vergleich zu normalen `def`s umgeht, fahren Sie fort. ### Pfadoperation-Funktionen
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