Sua equipe está dividida em métricas de desempenho do modelo de ML. Como você pode garantir que todos concordem?
Quando sua equipe debate sobre aprendizado de máquina (ML) métricas de desempenho do modelo, é vital alinhar a compreensão e os objetivos de todos. Para harmonizar perspectivas:
- Estabelecer uma definição compartilhada de indicadores-chave de desempenho (KPIs) para o modelo.
- Incentive a discussão aberta sobre as preferências de métricas e o raciocínio por trás delas.
- Implemente um período de teste para diferentes métricas para avaliar seu impacto prático no projeto.
Como você facilita o consenso em sua equipe sobre questões técnicas?
Sua equipe está dividida em métricas de desempenho do modelo de ML. Como você pode garantir que todos concordem?
Quando sua equipe debate sobre aprendizado de máquina (ML) métricas de desempenho do modelo, é vital alinhar a compreensão e os objetivos de todos. Para harmonizar perspectivas:
- Estabelecer uma definição compartilhada de indicadores-chave de desempenho (KPIs) para o modelo.
- Incentive a discussão aberta sobre as preferências de métricas e o raciocínio por trás delas.
- Implemente um período de teste para diferentes métricas para avaliar seu impacto prático no projeto.
Como você facilita o consenso em sua equipe sobre questões técnicas?
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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.
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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.
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I'm writing important points as mentioned below: ⭐ Clearly Define Our Goals: We need to agree on exactly what we're measuring. Are we looking for accuracy, how well the model predicts specific outcomes, or something else? Let's use clear examples to make sure everyone understands. ⭐ Regular Team Meetings: Need to schedule regular meetings specifically to discuss how our models are performing. Everyone should feel comfortable sharing their thoughts and concerns. ⭐ Focus on the "Why": We can talk about the reasons behind choosing specific metrics. This will help us align our goals and make sure we're all working towards the same objectives.
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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!
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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.
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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.
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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.
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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.
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- Metrics guide choices. - Think about the future - Try different options - Use real examples - Make sure goals match data - Keep it simple
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I will facilitate consensus by first establishing a shared understanding of key objectives and defining clear KPIs relevant to the problem. I encourage open discussions where team members share their preferred approaches and the rationale behind their choices. To ensure practical alignment, I propose implementing trial periods for different metrics or solutions, allowing the team to assess their effectiveness in real scenarios. This approach promotes data-driven decisions and fosters collaboration.
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