September 2024
Intermediate to advanced
408 pages
13h 30m
Spanish
book
Patrones de diseño de aprendizaje automático
by Valliappa Lakshmanan, Sara Robinson, Michael Munn
Content preview from Patrones de diseño de aprendizaje automático
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Capítulo 5. Patrones de diseño para un servicio resistente
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El objetivo de un modelo de aprendizaje automático es utilizarlo para hacer inferencias sobre datos que no ha visto durante el entrenamiento. Por lo tanto, una vez que se ha entrenado un modelo, normalmente se despliega en un entorno de producción y se utiliza para hacer predicciones en respuesta a las solicitudes entrantes. Se espera que el software que se despliega en entornos de producción sea resistente y requiera poca intervención humana para mantenerlo en funcionamiento. Los patrones de diseño de este capítulo resuelven los problemas asociados a la capacidad de recuperación en diferentes circunstancias, en lo que se refiere a los modelos ML de producción.
El patrón de diseño Función Servidora sin estado permite a la infraestructura servidora escalar y gestionar miles o incluso millones de solicitudes de predicciones por segundo. El patrón de diseño Batch Serving permite a la infraestructura de servicio gestionar de forma asíncrona solicitudes ocasionales o periódicas de millones a miles de millones de predicciones. Estos patrones son útiles más allá de la resiliencia, ya que reducen el acoplamiento entre creadores y usuarios de modelos de aprendizaje automático.
El patrón de diseño Evaluación continua del modelo se ocupa del problema habitual de detectar cuándo un modelo desplegado ya no es adecuado ...
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