{
  "id": "1310.5426",
  "version": "v2",
  "published": "2013-10-21T04:58:11.000Z",
  "updated": "2013-10-25T22:08:12.000Z",
  "title": "MLI: An API for Distributed Machine Learning",
  "authors": [
    "Evan R. Sparks",
    "Ameet Talwalkar",
    "Virginia Smith",
    "Jey Kottalam",
    "Xinghao Pan",
    "Joseph Gonzalez",
    "Michael J. Franklin",
    "Michael I. Jordan",
    "Tim Kraska"
  ],
  "categories": [
    "cs.LG",
    "cs.DC",
    "stat.ML"
  ],
  "abstract": "MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2013-10-25T22:08:12.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "distributed machine learning",
      "common machine learning algorithms",
      "initial results",
      "build distributed implementations",
      "wide variety"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2013arXiv1310.5426S"
    }
  }
}