En este curso, se presentan a los participantes las herramientas y prácticas recomendadas de MLOps para implementar, evaluar, supervisar y operar sistemas de AA de producción en Google Cloud. Las MLOps son una disciplina enfocada en la implementación, prueba, supervisión y automatización de sistemas de AA en producción. Los ingenieros profesionales de aprendizaje automático usan herramientas para mejorar y evaluar continuamente los modelos implementados. Trabajan con científicos de datos (o pueden serlo) que desarrollan modelos para ofrecer velocidad y rigor en la implementación de modelos con el mejor rendimiento.
MLOps (Machine Learning Operations) Fundamentals en español
MLOps (Machine Learning Operations) Fundamentals en españolGoogle Cloud
About this Course
What you will learn
Identify and use core technologies required to support effective MLOps.
Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
Adopt the best CI/CD practices in the context of ML systems.
Implement reliable and repeatable training and inference workflows.
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
Introducción a MLOps Fundamentals
En este módulo, se proporciona una descripción general del curso
Cuándo y por qué emplear las MLOps
En este módulo, estudiaremos el aprendizaje automático desde la perspectiva de las operaciones, lo que significa observar todo el sistema, desde definir el problema hasta diseñar la solución.
Información sobre los componentes principales de Kubernetes (opcional)
Este módulo es opcional.
Introducción a AI Platform Pipelines
En este módulo, abordaremos AI Platform Pipelines, un producto de Google Cloud que se usa para que las MLOps sean más sencillas, escalables y no presenten interrupciones con los servicios de Google Cloud.
Entrenamiento, ajuste y entrega en AI Platform
En este módulo, aprenderemos a entrenar, ajustar y entregar un modelo de forma manual desde el notebook de Jupyter en AI Platform.
Kubeflow Pipelines en AI Platform
En este módulo, automatizaremos el proceso de entrenamiento y ajuste que describimos antes con la ayuda de una canalización de Kubeflow Pipelines. En lugar de activar cada paso del proceso en forma manual desde el notebook de Jupyterlab, podemos activar todo el proceso con un solo clic después de haber expresado los distintos pasos como una canalización de Kubeflow Pipelines.
CI/CD para Kubeflow Pipelines en AI Platform
En este módulo, analizaremos el uso de CI/CD en canalizaciones de Kubeflow Pipelines. Sabemos cómo compilar una canalización de Kubeflow Pipelines automatizada, pero ¿cómo podemos integrar la canalización en una pila de integración continua? El objetivo es volver a compilar los elementos de la canalización apenas se envía nuevo código de entrenamiento al repositorio correspondiente.
Este módulo es un resumen de lo que se enseñó en el curso.
Recursos del curso
Enlaces PDF a todos los módulos
Frequently Asked Questions
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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Last Updated: September 1, 2015
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