September 2024
Intermediate to advanced
402 pages
13h 3m
Spanish
Content preview from Equipos eficaces de aprendizaje automático
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Capítulo 6. Pruebas automatizadas: Pruebas de modelos ML
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En el capítulo anterior, vimos el precio que pagamos por no tener pruebas automatizadas en las soluciones de ML, y los beneficios que las pruebas aportan a los equipos en términos de calidad, flujo, carga cognitiva y satisfacción. Esbozamos los componentes básicos de una estrategia de pruebas completa y entramos en detalles sobre la primera categoría de pruebas: las pruebas de software.
En este capítulo exploraremos la siguiente categoría de pruebas: Las pruebas de modelos ML (o pruebas de modelos, para abreviar). Dado que los grandes modelos lingüísticos (LLM) han tomado el mundo por asalto, también cubriremos técnicas para probar LLM y aplicaciones LLM.
Además, exploraremos prácticas que complementan las pruebas de modelos ML, como la visualización y el análisis de errores, el cierre del bucle de recopilación de datos y el diseño de pruebas abierto-cerrado. También hablaremos brevemente de las pruebas de datos antes de concluir con los próximos pasos concretos que pueden ayudarte a implantar estas pruebas en tus sistemas de ML.
En este capítulo, nos centraremos en las pruebas fuera de línea a escala, y no cubriremos las técnicas de pruebas en línea (p. ej., pruebas A/B, bandidos, experimentos intercalados), ya que están bien tratadas en el estupendo libro de Chip Huyen Designing Machine Learning Systems ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.