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:
    non sort: The
    title: Path to Predictive Analytics and Machine Learning
  name:
    name part: Doherty, Conor
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Camina, Steven
    role:
      role term: author.
  name:
    name part: White, Kevin
    role:
      role term: author.
  name:
    name part: Orenstein, Gary
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2016
    edition: 1st edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (87 pages)
  abstract: In many companies today, discussions about predictive analytics and machine learning tend to overlook one critical component: implementation. This report will help you examine practical methods for building and deploying scalable, production-ready machine-learning applications. Leveraging machine-learning models in production, after all, separates revenue generation and cost savings from mere intellectual novelty. Product specialists from MemSQL describe several real-time use cases, including "operational" applications, where machine-learning models automate decision-making processes, as well as "interactive" applications, where machine learning informs decisions made by humans. You'll also explore modern data processing architectures and leading technologies available for data processing, analysis, and visualization. With this report, you'll find ways to: Build real-time data pipelines Process transactions and analytics in a single database Create custom real-time dashboards Redeploy batch models in real time Build real-time machine learning applications Prepare data pipelines for predictive analytics and machine learning Apply predictive analytics to real-time challenges Use techniques for predictive analytics in production Move from machine learning to artificial intelligence
  note: Doherty, Conor.
  note: Electronic resource.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web.
  note: Mode of access: World Wide Web.
  identifier: 9781491969687
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 220518
    record change date: 20260128222803.4
    record identifier: in00004231537