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Problem Framing - Google Developers

Quick summary of the Google Developers 'Problem Framing' Course within the machine learning foundation series.
Problem Framing - Google Developers
Photo by Markus Winkler / Unsplash

This Google developers course is part of the foundation series of short courses on the subject of machine learning. Quick summary of this problem framing course:

  • Problem Framing - Determine if machine learning is the right approach followed by framing the problem in machine learning language.
  • Understand the problem - Can machine learning, predictive or generative be used or is a non machine learning solution required?
    • Predictive ML - Uses large amount of training data to predict outputs. Outputs can be checked against reality. Use supervised, unsupervised or reinforcement learning models.
    • Generative AI - Generates output based on the user's input. Large language model or image generator is trained with lots of unlabelled data.
    • Is ML the right approach? Perhaps you have some simple non-ML solution? That could be used to compare against a possible ML approach and decide if the ML solution warrants the cost and extra complications.
    • Must have good data! - You need abundant data, trusted and collected in similar ways and with features that are predictive
  • Framing in ML terms
    • Define the ideal outcome and goal.
      • What is the output and type of output?
        • Predictive ML for classification or numerical prediction.
          • Classification Model - Output which class the input is in.
          • Regression Model - Output a numerical prediction.
        • Generative AI for generative new content.
    • Define success metrics.
  • Implementing a Model
    • There are pre-trained models you can use, such as at TensorFlow and Kaggle. Have to be careful that you have the exact data these models need.