Machine learning is transforming how we use real-time metrics by enabling predictive analytics instead of just reactive monitoring. We're experimenting with ML models that analyze live player data to predict churn risk, identify potential whales before they make their first purchase, forecast server load spikes, and detect emerging balance issues. The challenge is training models that work with streaming data and produce predictions fast enough to act on. We've tried approaches like online learning algorithms, pre-trained models with real-time feature extraction, and ensemble methods that combine historical patterns with live signals. Feature engineering is particularly tricky - deciding which real-time metrics to feed the models and how to handle the temporal aspect of gaming sessions. What ML techniques have you applied successfully to real-time game data? How do you handle model drift as player behavior evolves? What's your approach to A/B testing AI-driven interventions without disrupting the player experience?