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Machine Learning Life Cycle

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A Machine Learning Life Cycle, (abbreviated ML Life Cycle) is a conceptual framework of the cyclical process that ML projects go through. However, there is no agreement on what its various phases are. Nevertheless, the following elements are popular. In consecutive order:

  • Understand the problem
  • Collect the data
  • Wrangle the data
  • Develop the model
  • Deploy the model

These are the visions of various data/AI companies: