ML Pipeline Stability & Evaluation

Q: ML Pipeline Stability & Evaluation

This question evaluates a product manager's ability to diagnose and stabilize an end-to-end time-series ML pipeline, testing competencies in data ingestion, feature engineering, model training and serving, evaluation frameworks, and remediation planning.

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Question

Scenario: Stabilizing a Time-Series ML Pipeline

You are the product manager for a system that uses time-series machine learning to predict a numeric target (e.g., demand, usage, or risk) across multiple customer segments and horizons. Recently, online prediction accuracy has dropped and the system appears unstable.

Tasks

Diagnose the root cause across the following layers:
- Data ingestion
- Feature generation
- Model training and serving
Define the evaluation framework to quantify performance regressions:
- Offline and online evaluation setup
- Sampling strategy (time-aware and segment-aware)
- Metrics and statistical tests
Propose the remediation plan:
- Technical fixes per layer
- Process and operational changes to restore stability and prevent recurrence

Assumptions: forecasts are time-series regression (numeric), with weekly seasonality and multiple segments; predictions drive user-facing decisions and business KPIs.

ML Pipeline Stability & Evaluation

Scenario: Stabilizing a Time-Series ML Pipeline

Tasks

Solution

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