April 2025
Intermediate to advanced
388 pages
12h 49m
Portuguese (Portugal, Brazil)
Content preview from Conceber sistemas de aprendizagem automática
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Capítulo 10. Infraestrutura e ferramentas para MLOps
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Nos Capítulos 4 a 6, discutimos a lógica para o desenvolvimento de sistemas de ML. Nos Capítulos 7 a 9, discutimos as considerações para implementar, monitorizar e atualizar continuamente um sistema de ML. Até agora, assumimos que os profissionais de ML têm acesso a todas as ferramentas e infra-estruturas necessárias para implementar essa lógica e realizar essas considerações. No entanto, essa suposição está longe de ser verdadeira. Muitos cientistas de dados disseram-me que sabem o que é correto fazer com os seus sistemas de ML, mas não o conseguem fazer porque a sua infraestrutura não está configurada de forma a permitir-lhes fazê-lo.
Os sistemas de aprendizagem automática são complexos. Quanto mais complexo for um sistema, mais pode beneficiar de uma boa infraestrutura. A infraestrutura, quando configurada corretamente, pode ajudar a automatizar processos, reduzindo a necessidade de conhecimentos especializados e de tempo de engenharia. Isto, por sua vez, pode acelerar o desenvolvimento e a entrega de aplicações de ML, reduzir a área de superfície para bugs e permitir novos casos de utilização. No entanto, quando mal configurada, a infraestrutura é difícil de usar e cara de substituir. Neste capítulo, discutiremos como configurar corretamente a infraestrutura para sistemas de ML.
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