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: Designing machine learning systems
  name:
    name part: Manjengwa, Shingai
    role:
      role term: presenter.
  name:
    name part: Long, Tim
    role:
      role term: presenter.
  name:
    name part: Huyen, Chip
    role:
      role term: presenter.
  name:
    name part: Farah, Danny
    role:
      role term: presenter.
  name:
    name part: Singh, Devin
    role:
      role term: presenter.
  name:
    name part: Hibat-Allah, Mohamed
    role:
      role term: presenter.
  name:
    name part: Thaine, Patricia
    role:
      role term: presenter.
  name:
    name part: Sobhani, Parinaz
    role:
      role term: presenter.
  name:
    name part: O'Reilly (Firm)
    role:
      role term: publisher.
  type of resource: moving image
  genre: videorecording
  genre: Instructional films.
  genre: Nonfiction films.
  genre: Internet videos.
  origin info:
    place:
      place term: xx
    date issued: 2023
    edition: [First edition].
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 video file (3 hr., 5 min.)) : sound, color.
  abstract: The engineering domain is one of the fastest growing areas in the field of machine learning. Machine learning powers advanced and seamless features such as user recommendations, predictions, image and speech recognition, medical diagnosis, and even fun applications like creating art based on user input. All this is powered by a plethora of systems that require well-trained engineers to design, implement, and use. But technical know-how is only part of the equation. In addition to the wide variety of technologies ML engineers must learn (including TensorFlow, PyTorch, AWS, Azure, BigQuery and many others), they have to deal with challenges like lack of data or data that's poorly labeled, fit, or collected to begin with. Join some of the best minds working in the field to learn how to tackle the challenges of ingesting, labeling, and applying data to the correctly identified machine learning problems. Whether you're a new ML engineer or a seasoned pro, you'll gain tips and insights that will help you design systems that allow for advanced analytics, predictions, and diagnoses. What you'll learn and how you can apply it Learn how to work with LLMs to achieve optimized results Discover how to design data and ML systems for trust and scalability Understand the challenges and opportunities in designing for industry This recording of a live event is for you because... You're a data engineer, ML engineer, or data scientist. You want to effectively approach the data lifecycle from ingestion to labeling to solving problems with machine learning. Recommended follow-up: Read Designing Machine Learning Systems (book) Read Fundamentals of Data Engineering (book) Read Machine Learning Design Patterns (book).
  note: Shingai Manjengwa, Tim Long, Chip Huyen, Danny Farah, Devin Singh, Mohamed Hibat-Allah, Patricia Thaine, Parinaz Sobhani, presenters.
  subject:
    topic: Machine learning
  classification: Q325.5
  classification: 006.3/1
  related item:
    title info:
      title: AI superstream (O'Reilly (Firm))
  identifier: 0636920936091 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: ORMDA
    record creation date: 230523
    record change date: 20260123222459.4
    record identifier: in00004724883
    language of cataloging:
      language term: eng