ML Case-study Interview Question: Agile ML for Budget Optimization Amidst Volatile Supply and Demand.
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Case-Study question
A technology-driven platform faces volatile shifts in supply and demand. An internal team needs to optimize budget allocation by predicting and balancing these shifts. The project involves various stakeholders, uncertain external factors, and time-limited offers by partners. The leadership expects a machine learning solution that accounts for data constraints, unpredictable events, and the need for agile collaboration. How would you plan, design, and implement this solution to optimize budget usage while dealing with uncertain market dynamics?
Detailed Solution
Setting up the problem requires identifying stakeholders, defining the project scope, and establishing clear roles. Access to external data like weather patterns and competitor campaigns might be limited. Proceed by focusing on controllable factors, while acknowledging that unpredictable variables can occur only on specific days. An agile methodology with Kanban can handle dynamic requirements and adapt to changes in the project backlog.
Data Acquisition and Preprocessing
Clean available data related to normal days and separate out data from special days (e.g., severe weather, special offers, competitor-driven events). Use feature engineering to include known seasonality patterns. Verify data quality and completeness. Remove irrelevant information and unify multiple data sources into a consistent format.
Modeling Approach
Use a machine learning model that forecasts the expected demand or cost for each time period. Try regression models if the relationship is reasonably linear, or try gradient boosting or random forest if non-linear patterns are suspected. Retain separate models or configurations for special events, acknowledging limited training data for those days.
Budget Optimization
Use a cost function to distribute the budget across different time periods or campaigns.
C_i is the cost per unit for each campaign i. x_i is the number of budget units allocated. The goal is to reduce total spending while meeting a baseline demand or performance threshold. Introduce constraints to ensure minimal required budgets for essential allocations.
Agile Project Management
Split tasks into sprints. Host daily standups to ensure alignment. Document tasks and blockers in a Kanban board. Invite stakeholders for sprint reviews to gather feedback and pivot quickly if needed. Maintain a servant leadership style to empower the team and address uncertainties collectively.
Communication with Stakeholders
Share updates through online channels. Present modeling outcomes and potential budget savings. Demonstrate how the model handles normal days versus special scenarios. Encourage feedback to refine the scope or adjust assumptions. Keep project artifacts in a shared repository.
Project Closure
Inform stakeholders of final outcomes. Confirm that objectives are met. Incorporate final feedback into the model and archive documentation for future reference. Show how the system can be extended if new data becomes available.
What if you have limited access to competitor data?
Acknowledge the gap in competitor-specific features. Use surrogate measures like demand fluctuations recorded historically. Incorporate publicly available event calendars or known promotional periods. Experiment with dummy variables indicating potential competitor impact windows. Verify if your model’s results remain robust.
How would you ensure your model handles seasonality?
Identify periodic patterns within the time series. Capture these patterns with seasonal decomposition methods or time-based features (e.g., month_of_year, day_of_week). Retrain the model periodically or set up a rolling window to adapt to shifting behavior over time. Compare performance on different seasonal segments to verify accuracy.
How do you handle the unpredictable factors like extreme weather?
Categorize them as out-of-scope events if data is inaccessible. Mark them separately and exclude them from normal model training. Maintain a fallback approach that forecasts minimal performance or triggers special alerts when these events occur. Log these anomalies and feed them back into the model for potential future refinement if partial data becomes available.
How do you ensure the project plan remains flexible?
Use agile rituals such as sprint retrospectives to discuss changes. Keep backlog tasks updated in Kanban. Move tasks between columns based on shifting priorities. Maintain continuous communication with data engineering, business stakeholders, and leadership to realign deliverables as new information arises.
What technical checks do you perform to validate the model?
Split datasets into training, validation, and test sets. Track metrics like MAE or RMSE for regression-based predictions. Monitor overfitting by comparing performance on training vs test sets. Perform cross-validation to confirm model stability. Conduct error analysis to pinpoint if certain weeks or event days consistently produce large residuals. Adjust the feature set if needed.
How do you ensure smooth deployment and monitoring?
Automate model deployment using CI/CD pipelines. Containerize the model so it can run consistently in staging and production. Schedule retraining or refreshing of data if the environment is dynamic. Create dashboards to observe real-time performance metrics and budget consumption. Alert stakeholders when performance deviates from expected thresholds.
How would you approach ongoing maintenance?
Set up periodic data ingestion checks. Schedule model retraining or incremental updates if distribution shifts. Archive older models for auditing or rollback purposes. Maintain logs of hyperparameters and training data versions. Communicate with stakeholders when performance decays or new requirements emerge.