rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Personalizing the infinite jukebox
    sub title: ML and the TensorFlow ecosystem at Spotify
  name:
    name part: Baer, Josh
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Dai, Keshi
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: moving image
  genre: videorecording
  genre: Electronic videos.
  origin info:
    place:
      place term: xx
    date issued: 2020
    edition: 1st edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 video file, approximately 40 min.)
  abstract: When Spotify launched in 2008, the lucky first launch countries rejoiced at the prospect of an almost infinite jukebox at their fingertips. In the 10+ years that followed, the product evolved quite a bit from something that required you to know exactly what you wanted to listen to before you listened to the product today that offers countless recommendations and a personalized experience. It's no surprise that ML has had a prominent role in that evolution. Josh Baer and Keshi Dai explain how Spotify applied ML to personalize its product and discuss the historical challenges of bringing ML products to market. You'll learn how Spotify uses TensorFlow and, especially, the TFX family of products as a "paved" workflow and how this has improved the ability for product teams to leverage ML in their work. You'll also examine the current state of the ML platform at Spotify and the open challenges the company faces. Prerequisite knowledge A basic understanding of the ML workflow and the challenges that engineers face in productionizing ML in the industry What you'll learn Understand the usage of Tensorflow Extended (tf.Transform, TF Data Validation, TF Model Analysis, tf.Examples, etc.) in the enterprise and how ML works at Spotify
  note: Baer, Josh.
  note: Videorecording.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web.
  note: Mode of access: World Wide Web.
  identifier: 0636920373643
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 200220
    record change date: 20260129212551.7
    record identifier: in00004258132