# Summary

<br />

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](https://developers.google.com/machine-learning/problem-framing/try-it/framing-exercise)
- [Formulating a solution](https://developers.google.com/machine-learning/problem-framing/try-it/formulate-exercise)

## Responsible AI

When implementing ML solutions, always follow
[Google's Responsible AI Principles](https://ai.google/responsibility/principles).

For a hands-on introduction for improving fairness and mitigating bias in
ML, see the [MLCC Fairness module](https://developers.google.com/machine-learning/crash-course/fairness).

## Keep learning

### More ML learning resources

- [Data Preparation and Feature Engineering](https://developers.google.com/machine-learning/data-prep)
- [Testing and Debugging in Machine Learning](https://developers.google.com/machine-learning/testing-debugging)
- [People + AI Research](https://pair.withgoogle.com/)