Summary

Framing a problem in terms of ML is a two-step process:

1. Verify that ML is a good approach by doing the following:

   • Understand the problem.
   • Identify a clear use case.
   • Understand the data.

2. Frame the problem in ML terms by doing the following:

   • Define the ideal outcome and the model's goal.
   • Identify the model's output.
   • Define success metrics.

These steps can save time and resources by setting clear goals and providing a shared framework for working with other ML practitioners.

Use the following exercises to frame an ML problem and formulate a solution:

• Framing an ML problem
• Formulating a solution

Privacy and ethics

Using ML can bring up privacy and ethical concerns. Before productionalizing a model, review the following resources:

• MLCC Fairness module

• Responsible AI practices

Keep learning

More ML learning resources

• Data Preparation and Feature Engineering
• Testing and Debugging in Machine Learning
• People + AI Research