Design comment-likelihood prediction platform
Scenario
You are tasked with designing an ML platform component that predicts the likelihood a user will comment on a given post. The machine learning model itself can be treated as a black box; the focus of this design exercise is on the underlying ML infrastructure, including feature pipelines, feature store, training and serving mechanisms, inference, and overall production concerns.
Goals
The system must be an end-to-end solution supporting:
- Offline training data generation and model training.
- Online inference for real-time scoring, used by various product surfaces such as feed ranking and notification candidate scoring.
- A comprehensive feature store strategy that accommodates both offline and online access patterns.
- Robust monitoring, logging, and mechanisms for continuous iteration and improvement.
Requirements & Constraints
- High QPS Online Scoring: The system must handle potentially tens of thousands of queries per second for online inference.
- P95 Latency Budget: The end-to-end latency for scoring should be within a 50–150 ms P95 budget. Candidates should state their specific assumption within this range.
- Feature Freshness: Certain features require near-real-time updates, with freshness requirements ranging from seconds to minutes.
- Avoid Training/Serving Feature Skew: The system must mitigate discrepancies between features used during training and those used during serving.
- Cold Start Handling: The design should address how to handle new users and posts effectively.
- A/B Testing and Safe Rollout: The platform must support A/B testing of models and features, along with safe, controlled model rollouts.
Deliverables
Candidates are expected to explain the design for the following components:
- Data sources and event logging mechanisms.
- Feature engineering processes and the design of the feature store.
- The training pipeline, including strategies for dataset versioning.
- The online inference architecture, covering aspects like batch vs. real-time processing, caching strategies, and fallbacks.
- Monitoring (for both data and model performance), retraining triggers, and overall reliability considerations.
AI interview prep
Core dilemma
Balancing the need for high-throughput, low-latency online inference with the complexity of maintaining feature freshness, consistency (avoiding skew), and robust offline training pipelines, all while ensuring scalability and reliability.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design comment-likelihood prediction platform
Scenario
You are tasked with designing an ML platform component that predicts the likelihood a user will comment on a given post. The machine learning model itself can be treated as a black box; the focus of this design exercise is on the underlying ML infrastructure, including feature pipelines, feature store, training and serving mechanisms, inference, and overall production concerns.
Goals
The system must be an end-to-end solution supporting:
- Offline training data generation and model training.
- Online inference for real-time scoring, used by various product surfaces such as feed ranking and notification candidate scoring.
- A comprehensive feature store strategy that accommodates both offline and online access patterns.
- Robust monitoring, logging, and mechanisms for continuous iteration and improvement.
Requirements & Constraints
- High QPS Online Scoring: The system must handle potentially tens of thousands of queries per second for online inference.
- P95 Latency Budget: The end-to-end latency for scoring should be within a 50–150 ms P95 budget. Candidates should state their specific assumption within this range.
- Feature Freshness: Certain features require near-real-time updates, with freshness requirements ranging from seconds to minutes.
- Avoid Training/Serving Feature Skew: The system must mitigate discrepancies between features used during training and those used during serving.
- Cold Start Handling: The design should address how to handle new users and posts effectively.
- A/B Testing and Safe Rollout: The platform must support A/B testing of models and features, along with safe, controlled model rollouts.
Deliverables
Candidates are expected to explain the design for the following components:
- Data sources and event logging mechanisms.
- Feature engineering processes and the design of the feature store.
- The training pipeline, including strategies for dataset versioning.
- The online inference architecture, covering aspects like batch vs. real-time processing, caching strategies, and fallbacks.
- Monitoring (for both data and model performance), retraining triggers, and overall reliability considerations.