Aligning teams on ML metrics requires both technical clarity and stakeholder empathy. Map each stakeholder's primary concerns - engineers focus on technical metrics like RMSE while product managers prioritize business KPIs. Create a unified dashboard that bridges these perspectives. Use a tiered approach: Base metrics (accuracy, precision, recall) Business impact metrics (revenue lift, user engagement) Operational metrics (inference time, resource usage) We implemented bi-weekly metric reviews where stakeholders shared key concerns, leading to a balanced scorecard satisfying both technical and business needs. The key is making metrics tangible to everyone while maintaining focus on shared goals.

I think the key point to act upon is, are those metrics contributing towards giving more clarity or transparency wrt to the results achieved. Which one of them is going to help convince my client/business, explain the decision making better, or can help in drawing some conclusions - prioritise that over the others. End of the day, goal of you and your team is to deliver the solutions to your client on various facets, pick the one which can fulfill a majority of them outright. No metric is bad or no suggestion is bad, but choose the one which is going to have an impact!

We should define clear goals to team on agile basis,also we should check on the data availability and ML models plan and suggest to the team.

Usually, this isn't a situation where conflict should arise. I would first try to understand where the conflict originated from. Was the requirement unclear? Was the team aware of the goal? The evaluation matrix for an ML model changes depending on the requirements and goals, so it's important to know if the goals changed mid-project. Once the reason is identified, I would address the team to align them towards the common goal and set proper requirement guidelines. Facilitating an open channel for communication and scheduling regular discussions should help bridge any gaps in understanding. This approach should help resolve conflicts and establish harmony within the team.

The choice of model performance metrics depends on the type of task and business context. Based on my experience, to resolve disagreements, the following steps can be taken: 1. Clarify objectives and context: Define the model’s goal and application, ensuring everyone understands its purpose. 2. Align on definitions and metric concepts: Discuss and agree on common metrics like accuracy or recall. 3. Prioritise metrics: Rank metrics based on business needs (e.g., prioritise recall in healthcare). 4. Make data-driven decisions: Evaluate metrics through experiments and business feedback. 5. Foster open discussions and a habit of documentation: Encourage open discussions and record outcomes for future reference.