- Simplified Structure: A lean model has a simpler architecture compared to 

    more complex models, making it easier to understand and implement.

- Focused Features: It focuses only on the most essential features or 

    variables relevant to the problem at hand, reducing unnecessary complexity. (Creating the MVP or Minimum Viable Product)

- Efficient Resource Usage: It optimizes resource utilization, such as memory 

    and computation, to achieve good performance with minimal overhead.

- Faster Development: Lean models are quicker to develop, as they involve 

    fewer components and require less fine-tuning compared to larger models.

- Easier Maintenance: With fewer moving parts, lean models are easier to 

    maintain and update over time.

- Example: Suppose you're building a spam email classifier. 

    A lean model might use a simple bag-of-words representation of the email 

    content and a logistic regression classifier, focusing on key words and 

    features that distinguish spam from non-spam emails, rather than using 

    more complex deep learning architectures.