Improve AI/ML Model with help of Domain Experts

When working on building and maintaining a machine learning model, asking the right questions to domain experts is crucial for understanding the problem, gathering the right data, and ensuring the model meets the desired objectives. Here are some essential questions to ask:

Understanding the Problem

What is the primary goal of the project?

What specific problem are we trying to solve with this machine learning model?

What are the key business objectives and metrics?

How will the success of the model be measured in terms of business impact?

Who are the end-users of this model?

What are their needs and expectations?

Data Collection and Preparation

What data is currently available?

• What data sources can we use?
• Are there any constraints or limitations on data access?

How is the data structured and labeled?

• What are the key features and labels in the dataset?
• Are there any missing or inconsistent values in the data?

What is the quality and reliability of the data?

• Are there any known issues with the data quality?
• How frequently is the data updated?

Domain Knowledge

What domain-specific knowledge is crucial for this problem?

Are there any industry standards or best practices we should be aware of?

Are there any important features or variables that should be included in the model?

What features have the most significant impact on the outcome?

Are there any known relationships or interactions between features?

How do these relationships influence the target variable?

Model Building

What types of models have been tried before, if any?

What were the outcomes and limitations of previous models?

What are the key challenges in this domain?

Are there specific pitfalls or common issues we should anticipate?

Model Evaluation and Validation

What evaluation metrics are most relevant for this problem?

How should we balance different metrics (e.g., precision vs. recall)?

Are there any benchmark models or performance standards?

What is the expected or acceptable level of performance?

Deployment and Maintenance

How will the model be integrated into the existing system?

What are the requirements for model deployment?

What are the constraints on model latency and throughput?

How quickly does the model need to produce results?

What is the plan for model monitoring and maintenance?

• How will we track the model’s performance over time?
• What processes are in place for model retraining and updating?

Regulatory and Ethical Considerations

Are there any regulatory requirements or constraints?

How do these affect data usage and model deployment?

What ethical considerations should we keep in mind?

How do we ensure the model is fair and unbiased?

Communication and Collaboration

Who are the key stakeholders and how should we communicate with them?

How often should we provide updates on the model’s progress?

What collaboration tools and processes should we use?

How can we ensure effective collaboration between domain experts and the data science team?

By asking these questions, you can gather valuable insights from domain experts that will help guide the development, evaluation, and maintenance of a machine learning model.