April 2025
Intermediate to advanced
362 pages
11h 24m
Portuguese (Portugal, Brazil)
book
Arquitetar plataformas de dados e de aprendizagem automática
by Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner
Content preview from Arquitetar plataformas de dados e de aprendizagem automática
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Capítulo 11. Arquitetar uma plataforma de ML
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No capítulo anterior, discutimos a arquitetura geral das aplicações de ML e que, em muitos casos, irá utilizar modelos de ML pré-construídos. Em alguns casos, a sua equipa terá de desenvolver o modelo de ML que está no centro da aplicação de ML.
Neste capítulo, vais aprofundar o desenvolvimento e a implementação desses modelos de ML personalizados. Analisará as fases de desenvolvimento dos modelos de ML e as estruturas que suportam esse desenvolvimento. Depois de o modelo ter sido criado, terás de automatizar o processo de formação, analisando as ferramentas e os produtos que podem ajudar-te a fazer esta transição. Por fim, terás de monitorizar o comportamento dos modelos treinados que foram implementados nos pontos finais para ver se estão a desviar-se ao fazer inferências.
Nos capítulos anteriores, discutimos as capacidades de AM que são possibilitadas por várias partes da plataforma de dados. Especificamente, o armazenamento de dados para a sua plataforma de ML pode estar no lago de dados(Capítulo 5) ou no DWH(Capítulo 6), o treino seria efectuado em computação eficiente para esse armazenamento e a inferência pode ser invocada a partir de um pipeline de streaming(Capítulo 8) ou implementada no edge(Capítulo 9). Neste capítulo, vamos reunir todas estas discussões e considerar o que está incluído nestas capacidades ...
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