Problem framing is the process of analyzing a problem to isolate the individual elements that need to be addressed to solve it. Problem framing helps determine your project's technical feasibility and provides a clear set of goals and success criteria. When considering an ML solution, effective problem framing can determine whether or not your product ultimately succeeds.

Formal problem framing is the critical beginning for solving an ML problem, as it forces us to better understand both the problem and the data in order to design and build a bridge between them. - TensorFlow engineer

At a high level, ML problem framing consists of two distinct steps:

  1. Determining whether ML is the right approach for solving a problem.
  2. Framing the problem in ML terms.

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Why is problem framing important?
Problem framing ensures that an ML approach is a good solution to the problem before beginning to work with data and train a model.
Problem framing helps diagnose problems with existing ML models and uncovers issues with data.