Building Machine Learning pipelines with Docker Containers

Building Machine Learning pipelines with Docker Containers


The biggest impact on machine learning right now is not coming from a new algorithm,a new approach or statistical method. It’s coming from Docker containers. Docker containers help to solve a variety of problems:  they make it easy to use libraries with complicated setups; they make your output reproducible; they make it easier to share your work, and they can take the pain out of the ML  stack.


En este curso interactivo, los participantes  aprenderán a iniciarse en ML con casos prácticos en contenedores Docker:

  • Utilizar contenedores Docker con ML
  • Deep Learning y Docker
    • Lab: Deep learning  with Jupyter-driven Docker Containers
  • Docker y GPUs: Uso de nvidia-docker para Deep learning
    • Lab: How to Fake It As an Artist with Docker and Deep Learning
  • Microservicios, DevOps y ML
  • Casos de uso para pipelines en ML
  • Diseñar pipelines en ML basados en contenedores Docker
  • Cómo usar las librerias más comunes en ML con Docker (TensorFlow, Theano, Torch,Keras, Neon, Caffe)
  • Uso de Docker y para acelerar proyectos ML
    • Lab: Detectar noticias falsas con ML y Docker
  • Pipelines NLP con Docker
    • Lab:  Crear un índice de Fear and Greed con ML y Docker
  • Crear recomendaciones de motores usando contenedores Docker
    • Lab: Recommendation engine serving recommendations via  PubNub queues with Docker
  • Porqué necesitamos las pipelines en ML

Course Features

  • Lectures 0
  • Quizzes 0
  • Language English
  • Students 0
  • Assessments Self
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