2–5 years · Mid — Python · Statistics · scikit-learn · Feature engineering · Model evaluation · Deployment
Examines the training data for skewness or outlier effects; checks for data leakage or improper target encoding; evaluates the model's assumptions and performance metrics on different segments; considers alternative algorithms if necessary.
What role does the choice of loss function play in this bias?
Utilises data partitioning or sharding to improve access speeds; leverages in-memory data structures or databases; implements efficient data loading techniques such as lazy loading; evaluates batch processing to reduce overhead.
How do you handle feature engineering in a streaming data scenario?
Monitors the API logs to identify response bottlenecks; checks for model loading time and input data preprocessing efficiency; investigates the hardware and resource utilisation on the server; considers caching predictions for repeated requests.
If the model is too large to cache, what alternative solutions do you explore?
Review model performance metrics over time to identify trends; check for concept drift using statistical tests; compare recent input data distributions to training data; evaluate external factors influencing predictions; determine if retraining is necessary and plan for data collection.
You discover significant changes in user behaviour affecting the model's primary features. How do you adjust your strategy to mitigate this?
Checks the data for concept drift; compares the incoming data distribution to the training data; evaluates model predictions against new labels if available; considers retraining the model with updated data or features.
What tools or methods do you use to detect concept drift?
Specifies the project context and model type; identifies the chosen metric and justifies its relevance to the business problem; discusses alternative metrics considered and why they were not selected; reflects on any surprises during evaluation post-deployment; avoids vague references to team consensus.
How did you validate the effectiveness of your chosen metric?
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