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.
MLOps (Machine Learning Operations) Fundamentals Português
MLOps (Machine Learning Operations) Fundamentals PortuguêsGoogle Cloud
About this Course
Ter concluído o curso Machine Learning with Google Cloud ou contar com experiência equivalente
What you will learn
Identificar e usar as principais tecnologias para dar suporte a MLOps eficazes
Adotar as práticas recomendadas de CI/CD no contexto de sistemas de ML
Configurar e provisionar arquiteturas do Google Cloud para ambientes MLOps confiáveis e eficazes
Operar modelos de aprendizado de máquina implantados de maneira eficaz e eficiente
Ter concluído o curso Machine Learning with Google Cloud ou contar com experiência equivalente
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
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.
Este módulo é um resumo dos temas do curso
Recursos do curso
Links dos PDFs de todos os módulos
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Last Updated: September 1, 2015
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