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.


In this instructor-led  live training, participants will learn how to get started with machine learning in containers through practical hands-on labs:

  • Using Docker containers for ML
  • Deep Learning and Docker
    • Lab: Deep learning  with Jupyter-driven Docker Containers
  • Docker and GPUs: Using nvidia-docker for Deep learning
    • Lab: How to Fake It As an Artist with Docker and Deep Learning
  • Microservices, DevOps and ML
  • Use Cases for ML pipelines
  • Designing ML pipelines based on Docker Containers
  • How to use most common ML libraries with Docker (TensorFlow, Theano, Torch,Keras, Neon, Caffe)
  • Using Docker and for accelerating ML projects
    • Lab: Detect fake news with ML and Docker
  • NLP pipelines with Docker
    • Lab:  Building a Fear and Greed Index with ML and Docker
  • Building recommendation engines using Docker containers
    • Lab: Recommendation engine serving recommendations via  PubNub queues with Docker
  • Why do we need ML pipelines?

Course Features

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