MLOps (Machine Learning Operations) Fundamentals Português

بواسطة: Coursera

Overview

Neste curso, os participantes vão conhecer as ferramentas de MLOps e as práticas recomendadas para a implantação, avaliação, monitoramento e operação de sistemas de ML de produção no Google Cloud. MLOps é uma disciplina com foco na implantação, teste, monitoramento e automação de sistemas de ML em produção. Profissionais de engenharia de machine learning usam ferramentas para fazer melhorias contínuas e avaliações de modelos implantados. Eles trabalham com (e podem até ser) cientistas de dados que desenvolvem modelos para garantir a velocidade e o rigor na implantação de modelos com melhor desempenho.

Syllabus

  • Introdução às noções básicas de MLOps
    • Este módulo apresenta uma visão geral do curso
  • Por que e quando usar MLOps
    • Neste módulo, vamos abordar o machine learning por uma perspectiva operacional. Isso significa ter uma visão de todo o sistema, desde a definição do problema até a solução.
  • Noções básicas sobre os principais componentes do Kubernetes (opcional)
    • Este módulo é opcional
  • Introdução ao AI Platform Pipelines
    • Neste módulo, vamos falar sobre um produto do Google Cloud, o AI Platform Pipelines, que facilita, otimiza e escalona o MLOps com os Serviços do Google Cloud.
  • Treinamento, ajuste e exibição no AI Platform
    • Neste módulo, vamos conferir como treinar, ajustar e exibir um modelo manualmente em um notebook do Jupyter na AI Platform.
  • Kubeflow Pipelines no AI Platform
    • Neste módulo, vamos automatizar o processo de treinamento e ajustes descrito anteriormente usando um pipeline do Kubeflow. Em vez de ter que acionar cada passo do processo manualmente no notebook do Jupyterlab, podemos fazer isso com todo o processo em um único clique depois de expressar as várias etapas como um pipeline do Kubeflow.
  • CI/CD do Kubeflow Pipelines no AI Platform
    • Neste módulo, vamos abordar o CI/CD para pipelines do Kubeflow. Sabemos como criar um pipeline automatizado do Kubeflow, mas como podemos integrá-lo a uma pilha de integração contínua? A meta é recriar os recursos do pipeline imediatamente quando um novo código de treinamento for enviado para o repositório correspondente.
  • Resumo
    • Este módulo é um resumo dos temas do curso
  • Recursos do curso
    • Links dos PDFs de todos os módulos

Taught by

Google Cloud Training

MLOps (Machine Learning Operations) Fundamentals Português
الذهاب الي الدورة

MLOps (Machine Learning Operations) Fundamentals Português

بواسطة: Coursera

  • Coursera
  • مجانية
  • Portuguese
  • متاح شهادة
  • متاح في أي وقت
  • intermediate
  • Portuguese, English
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