Problem Identification#

Problem identification is a crucial step in the process of monitoring and continuous improvement of models. It involves identifying and defining the specific issues or challenges faced by deployed models in real-world scenarios. By accurately identifying the problems, organizations can take targeted actions to address them and improve model performance.

Key Steps in Problem Identification:

• Data Analysis: Conduct a comprehensive analysis of the available data to understand its quality, completeness, and relevance to the model's objectives. Identify any data anomalies, inconsistencies, or missing values that may affect model performance.

• Performance Discrepancies: Compare the predicted outcomes of the model with the ground truth or expected outcomes. Identify instances where the model's predictions deviate significantly from the desired results. This analysis can help pinpoint areas of poor model performance.

• User Feedback: Gather feedback from end-users, stakeholders, or domain experts who interact with the model or rely on its predictions. Their insights and observations can provide valuable information about any limitations, biases, or areas requiring improvement in the model's performance.

• Business Impact Assessment: Assess the impact of model performance issues on the organization's goals, processes, and decision-making. Identify scenarios where model errors or inaccuracies have significant consequences or result in suboptimal outcomes.

• Root Cause Analysis: Perform a root cause analysis to understand the underlying factors contributing to the identified problems. This analysis may involve examining data issues, model limitations, algorithmic biases, or changes in the underlying environment.

• Problem Prioritization: Prioritize the identified problems based on their severity, impact on business objectives, and potential for improvement. This prioritization helps allocate resources effectively and focus on resolving critical issues first.

By diligently identifying and understanding the problems affecting model performance, organizations can develop targeted strategies to address them. This process sets the stage for implementing appropriate solutions and continuously improving the models to achieve better outcomes.