docker-for-data-science-reproducibility-and-deployment-hareem-naveed.json

{
  "abstract": "We will walk through a high level introduction to Docker and work\nthrough two use cases focused on reproducibility and model deployment.\nWorking Docker files and python code will be provided. In this tutorial\nwe will cover:\n\n-  Benefits of using Docker as part of your workflow.\n-  Anatomy of a Docker file.\n-  Containers and registries: build a Docker container, push and pull\n   from DockerHub.\n-  How to set up your development environment on any machine using\n   Docker.\n-  How to deploy a simple sklearn model using flask and Docker.\n\nAttendees will need: Docker, git.\n\nPlease install both docker and git prior to the tutorial.\n",
  "copyright_text": null,
  "description": "This tutorial will introduce the basics of working with Docker\ncontainers and walk through common use cases. Using Docker containers,\ndata scientists can create reproducible work flows and ease model\ndeployment.\n",
  "duration": 3695,
  "language": "eng",
  "recorded": "2018-10-21",
  "related_urls": [
    {
      "label": "schedule",
      "url": "https://pydata.org/la2018/schedule/"
    }
  ],
  "speakers": [
    "Hareem Naveed"
  ],
  "tags": [],
  "thumbnail_url": "https://i.ytimg.com/vi/gBalsA-x300/maxresdefault.jpg",
  "title": "Docker for Data Science: Reproducibility and Deployment",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=gBalsA-x300"
    }
  ]
}