nicole-carlson-turning-pymc3-into-scikit-learn.json

{
  "copyright_text": "Standard YouTube License",
  "description": "PyMC3 is a probabilistic modeling library. Most examples of how to use the library exist inside of Jupyter notebooks. However, making your model reusable and production-ready is a bit opaque. My goal is to show a custom Bayesian Model class that implements the sklearn API. After this talk, you should be able to build your own reusable PyMC3 models.",
  "duration": 1805,
  "language": "eng",
  "recorded": "2017-11-27",
  "related_urls": [
    {
      "label": "schedule",
      "url": "https://pydata.org/nyc2017/schedule/"
    },
    {
      "label": "slides",
      "url": "https://github.com/parsing-science/pydata_nyc_nov_2017"
    }
  ],
  "speakers": [
    "Nicole Carlson"
  ],
  "tags": [],
  "thumbnail_url": "https://i.ytimg.com/vi/zGRnirbHWJ8/maxresdefault.jpg",
  "title": "Turning PyMC3 into scikit learn",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=zGRnirbHWJ8"
    }
  ]
}