Balancing stakeholder demands for complex machine learning (ML) solutions with practicality is crucial for project success.

When stakeholders demand intricate ML solutions, it's essential to find a balance between their desires and what's feasible. Here’s how you can manage these expectations effectively:

• Communicate limitations clearly: Explain the constraints and potential trade-offs of complex ML models.

• Set realistic milestones: Break down the project into achievable phases to demonstrate progress and manage expectations.

• Involve stakeholders in the process: Regular updates and feedback loops can ensure everyone remains aligned.

How do you balance stakeholder expectations in your ML projects? Share your thoughts.