Start by clarifying requirements and scale, then walk through the ML lifecycle end-to-end, emphasizing how each component (feature pipeline, feature store, training, inference, monitoring) integrates with the others. Highlight trade-offs and Reddit-specific considerations like real-time engagement signals and cold-start users.
Pro tip: Anchor your design around the feature store as the central contract between offline and online, and explicitly discuss how you'd prevent training-serving skew and handle feature freshness for real-time commenting behavior.
Ask about scale (users, posts, QPS), latency requirements, data availability, and business goals (e.g., ranking comments, notifications). Define success metrics like AUC, precision@k, and online engagement lift.
Describe batch and streaming pipelines for user, post, and interaction features. Explain how a feature store (e.g., Feast, Tecton) serves consistent features offline for training and online for inference, with low-latency lookups.
Cover data splitting (time-based), model choices (e.g., gradient boosted trees, logistic regression, or deep models), and training infrastructure (distributed training, hyperparameter tuning). Emphasize handling class imbalance and temporal drift.
Design a low-latency prediction service that fetches features from the online store, runs the model, and returns a probability. Discuss caching, fallbacks, and scaling (e.g., Kubernetes, serverless).
Monitor data quality, feature drift, prediction drift, and model performance (delayed labels). Set up alerts, A/B testing, and a feedback loop to retrain models regularly.
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I talked about logging features at serving time and using those logs to reconstruct training data, which is the right idea.
Start by defining feature skew and its impact on model performance, then outline a systematic approach to detect, prevent, and monitor skew. Emphasize collaboration between ML and platform teams, and propose concrete tools and processes like a feature store and shared transformation code.
Pro tip: Highlight the importance of logging raw feature values at serving time and comparing them to training data distributions to catch skew early. Mention that at Reddit, where user behavior is dynamic, skew can arise from real-time interactions, so a robust monitoring system is crucial.
Explain what training/serving skew is and how to detect it using statistical tests (e.g., KL divergence) and monitoring dashboards comparing feature distributions.
Advocate for a feature store or shared transformation library to ensure identical feature computation logic in training and serving, reducing code duplication.
Implement unit tests for feature transformations and integration tests that simulate serving conditions, including edge cases like missing values or time-based features.
Set up real-time monitoring of feature distributions and model predictions, with alerts for significant deviations, and log raw features for offline analysis.
Establish a feedback loop where detected skew triggers investigation and updates to pipelines, and consider techniques like online learning or periodic retraining.
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Went with fallback features: content-based signals for posts, demographic or onboarding data for new users.
Start by clarifying the two distinct cold start problems: new users (personalization) and new posts (ranking). Then propose a layered solution that combines content-based signals, exploration strategies, and fallback mechanisms, while acknowledging trade-offs between relevance and diversity.
Pro tip: Emphasize that cold start is not a one-time problem but a continuous process—new users become warm, and new posts gain engagement—so design systems that adapt quickly and avoid overfitting to early signals.
Ask clarifying questions to distinguish between new user and new post cold start, and understand scale, latency, and business goals. This shows you can handle ambiguity.
For new users, use demographic, contextual, or onboarding data; for new posts, use content features (text, images, metadata) and author reputation. Avoid relying solely on collaborative filtering.
Implement multi-armed bandits or epsilon-greedy to explore new items, and fallback to popularity or trending content when signals are sparse. Ensure diversity to gather feedback.
Set up fast feedback loops to update models as soon as interactions occur. Use online learning or incremental updates to transition from cold to warm start.
Define metrics (e.g., CTR, engagement, diversity) and monitor cold start performance. A/B test strategies and be ready to adapt based on results.
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Shadow mode first, then traffic splitting with a feature flag layer, then full cutover with rollback capability.
Start by framing the problem around measurable impact and risk reduction, then walk through a phased rollout plan that includes offline evaluation, shadow deployment, canary release, and A/B testing. Emphasize how you'd instrument metrics, define guardrails, and automate rollback to ensure safety.
Pro tip: Tie every phase to a clear decision gate and success metric—this shows you think like an owner, not just a coder. Also, mention that you'd align with data science early to define the experiment's primary and guardrail metrics.
Work with product and data science to specify primary metrics (e.g., CTR, engagement) and guardrail metrics (e.g., latency, error rate, toxicity). Establish minimum detectable effect and required sample size.
Evaluate the new model on historical data and run it in shadow mode alongside the current model to compare predictions without affecting users. Check for regressions in key slices.
Deploy to a small percentage of traffic (e.g., 1-5%) and monitor system health and business metrics. Gradually increase traffic if metrics stay within guardrails.
Randomize users into control and treatment groups, run the experiment for the precomputed duration, and analyze results with appropriate statistical tests. Ensure no interference between groups.
Set up automated alerts and rollback triggers if guardrails are breached. After the experiment, document learnings and decide whether to fully launch, iterate, or abandon.
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Talked about input feature distribution drift, prediction score drift, and downstream engagement metrics as the ground truth signal.
Start by outlining a layered monitoring strategy covering data quality, model performance, and business impact. Then define specific triggers for retraining based on degradation thresholds, data drift, and business metrics. Emphasize the importance of automation and continuous evaluation.
Pro tip: Tie monitoring metrics directly to Reddit's key business metrics (e.g., engagement, retention) to show product sense. Also, mention that retraining triggers should be configurable and versioned to allow for safe rollbacks.
Cover data quality (input drift, missing values), model performance (accuracy, latency), and business metrics (CTR, user engagement).
Use statistical tests (e.g., KL divergence, PSI) to monitor feature distributions and detect covariate shift.
Track online metrics like prediction distribution and offline metrics like precision/recall on labeled data.
Trigger retraining when performance drops below threshold, significant data drift occurs, or on a scheduled basis (e.g., weekly).
Implement automated retraining pipelines with A/B testing and rollback capabilities, and continuously refine triggers.
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