Your team is divided on ML model performance metrics. How can you ensure everyone sees eye to eye?
When your team debates over machine learning (ML) model performance metrics, it's vital to align everyone's understanding and goals. To harmonize perspectives:
- Establish a shared definition of key performance indicators (KPIs) for the model.
- Encourage open discussion about metric preferences and the reasoning behind them.
- Implement a trial period for different metrics to assess their practical impact on the project.
How do you facilitate consensus in your team on technical matters?
Your team is divided on ML model performance metrics. How can you ensure everyone sees eye to eye?
When your team debates over machine learning (ML) model performance metrics, it's vital to align everyone's understanding and goals. To harmonize perspectives:
- Establish a shared definition of key performance indicators (KPIs) for the model.
- Encourage open discussion about metric preferences and the reasoning behind them.
- Implement a trial period for different metrics to assess their practical impact on the project.
How do you facilitate consensus in your team on technical matters?