CITATION.cff

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title: >-
  Intro to Google Cloud Platform (GCP) for Machine Learning
  and AI
message: >-
  Please cite this lesson using the information in this file
  when you refer to it in publications, and/or if you
  re-use, adapt, or expand on the content in your own
  training material.
type: dataset
authors:
  - given-names: Christopher
    family-names: Endemann
    email: endemann@wisc.edu
    affiliation: University of Wisconsin-Madison
    orcid: 'https://orcid.org/0000-0002-7357-6129'
repository-code: 'https://qualiamachine.github.io/Intro_GCP_for_ML/'
url: 'https://qualiamachine.github.io/Intro_GCP_for_ML/'
abstract: >-
  This workshop teaches core workflows for building,
  training, and tuning ML/AI models in Google Cloud's Vertex
  AI platform. Participants learn to set up data storage,
  configure Vertex AI Workbench notebooks as lightweight
  controllers, launch training and hyperparameter tuning
  jobs, and optimize resource costs effectively within GCP.
  The workshop also covers building retrieval-augmented
  generation (RAG) pipelines using Gemini models. Target
  audience: researchers and data scientists with basic
  Python and ML knowledge who want to run ML experiments on
  cloud infrastructure.
license: CC-BY-4.0