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  1. docs/en/docs/advanced/events.md

    ## Use Case { #use-case }
    
    Let's start with an example **use case** and then see how to solve it with this.
    
    Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖
    
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  2. docs/pt/docs/advanced/events.md

    ## Caso de uso { #use-case }
    
    Vamos começar 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. 🤖
    
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  3. docs/es/docs/advanced/events.md

    ## Caso de Uso { #use-case }
    
    Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.
    
    Imaginemos que tienes algunos **modelos de machine learning** que quieres usar para manejar requests. 🤖
    
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  4. docs/es/docs/async.md

    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).
    
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  5. docs/pt/llm-prompt.md

    * I/O (as in "input and output"): I/O (do not translate to "E/S")
    * JSON Schema: JSON Schema
    * library: biblioteca
    * lifespan: lifespan (do not translate to "vida útil")
    * list (as in Python list): list
    * Machine Learning: Aprendizado de Máquina
    * media type: media type (do not translate to "tipo de mídia")
    * non-Annotated: non-Annotated (do not translate non-Annotated when it comes after a Python version.e.g., “Python 3.10+ non-Annotated”)
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  6. docs/es/llm-prompt.md

    * API key: API key (do not translate to "clave API")
    * 100% test coverage: cobertura de tests del 100%
    * back and forth: de un lado a otro
    * I/O (as in "input and output"): I/O (do not translate to "E/S")
    * Machine Learning: Machine Learning (do not translate to "Aprendizaje Automático")
    * Deep Learning: Deep Learning (do not translate to "Aprendizaje Profundo")
    * callback hell: callback hell (do not translate to "infierno de callbacks")
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  7. docs/de/docs/_llm-test.md

    * <abbr title="Eine Gruppe von Maschinen, die so konfiguriert sind, dass sie verbunden sind und in irgendeiner Weise zusammenarbeiten.">Cluster</abbr>
    * <abbr title="Eine Methode des Machine Learning, die künstliche neuronale Netze mit zahlreichen versteckten Schichten zwischen Eingabe- und Ausgabeschicht verwendet und so eine umfassende interne Struktur entwickelt">Deep Learning</abbr>
    
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  8. docs/en/docs/_llm-test.md

    * <abbr title="Parallel Server Gateway Interface">PSGI</abbr>
    
    ### The abbr gives an explanation { #the-abbr-gives-an-explanation }
    
    * <abbr title="A group of machines that are configured to be connected and work together in some way.">cluster</abbr>
    * <abbr title="A method of machine learning that uses artificial neural networks with numerous hidden layers between input and output layers, thereby developing a comprehensive internal structure">Deep Learning</abbr>
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  9. docs/es/docs/_llm-test.md

    ### El abbr da una explicación { #the-abbr-gives-an-explanation }
    
    * <abbr title="Un grupo de máquinas configuradas para estar conectadas y trabajar juntas de alguna manera.">clúster</abbr>
    * <abbr title="Un método de machine learning que usa redes neuronales artificiales con numerosas capas ocultas entre las capas de entrada y salida, desarrollando así una estructura interna completa">Deep Learning</abbr>
    
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  10. docs/en/data/topic_repos.yml

      html_url: https://github.com/zhanymkanov/fastapi-best-practices
      stars: 14644
      owner_login: zhanymkanov
      owner_html_url: https://github.com/zhanymkanov
    - name: machine-learning-zoomcamp
      html_url: https://github.com/DataTalksClub/machine-learning-zoomcamp
      stars: 12320
      owner_login: DataTalksClub
      owner_html_url: https://github.com/DataTalksClub
    - name: fastapi_mcp
      html_url: https://github.com/tadata-org/fastapi_mcp
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