Post Comment Likelihood (ML Infra)

Design an ML infrastructure system to predict the likelihood of a user commenting on a post.

Background: Build the infrastructure to serve a model that predicts whether a user will comment on a given post. The model itself is a black box.

Key Components:

1. Feature Store - Store and serve features for real-time inference
2. Inference Service - Serve predictions in real-time
3. Model Registry - Manage model versions
4. Monitoring - Track model performance

Discussion Points:

• Feature engineering pipeline (offline vs online features)
• Real-time vs batch feature computation
• Low-latency inference requirements (<100ms)
• A/B testing infrastructure
• Model retraining pipeline
• Caching strategies for features
• Reliability and fault tolerance