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!

My team is following an experiment and theory approach to making and understanding new molecules. This should/could require firstly training ML to help develop efficient and reproducible synthetic protocols. The evaluation of the properties is probably straightforward in terms of ML but the theoretical evaluation has similar challenges to the synthetic aspects. Both require finding the best recipe for success. I like the analogy with inventing new dishes. The cook plays around with the ingredients, lets the guests evaluate the results and then the overall analysis of this by a "Mentalist" results in the foolproof recipe. This recipe uses the ingredients in the most effecient way and the best way to do the preparation guarantees success.

To facilitate consensus on ML metrics, I start by aligning the team on project goals, ensuring everyone understands what we’re optimizing for, whether it’s accuracy, fairness, or user experience. Then, we define key metrics like precision, recall, or F1-score in clear terms to avoid confusion. Open discussions are encouraged so team members can share their reasoning. To settle debates, I suggest experimenting with different metrics on real data to evaluate their impact. Data-driven insights often guide the team toward agreement. Once a decision is made, we document it and revisit as needed to adapt to evolving project needs.

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.

I've found it helpful for the team have a consensus on what is defined as DONE. They should have clear objectives, with regular meetings to discuss if the model performance aligns with the predefined objectives. Ultimately, it is important to keep focus on why the model was built and the metric that is best suited for the task should be considered first.

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.

As there are different metrics to evaluate an ML model, team debate might happen. Following are the things one should consider: - 1. Client's requirement: -> It might happen that your precision might matter more than accuracy which might align perfectly with client's requirement. 2. Metrics' role: -> Help your team member understand the role and information each metric conveys of your trained model. 3. Overall performance: -> Some members might give emphasize to a well performing metric, whereas some members want all metrics to perform in a balanced way. Have a thorough discussion about the cause of such phenomenon and recalibrate your approach. 4. Trust in process: -> A team is a team when they know what and why they are doing.

Aligning on ML Performance Metrics: 1. Define Shared KPIs: Collaboratively agree on the key metrics (e.g., accuracy, precision, recall, or F1-score) relevant to the project's goals. 2. Open Discussions: Create a space where team members can explain their metric preferences and the rationale. 3. Data-Driven Trials: Test multiple metrics in a trial phase to evaluate their real-world impact on decision-making. 4. Context Matters: Tailor metrics to the project's priorities, such as business value or fairness concerns. My View: Misalignment often stems from different priorities (e.g., technical vs. business). Clear communication and iterative evaluation can bridge this gap.