What is Prediction Caching?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Caching is valuable when three conditions are met: input patterns repeat frequently (same users, products, or queries appearing within cache TTL windows), predictions are deterministic for given inputs (same input always produces the same output), and model inference is expensive relative to cache lookup (GPU inference costs significantly more than Redis memory). Expected cache hit rates vary by application: search ranking (40-60% hit rate due to repeated popular queries), recommendation systems (20-40% for returning users), and document classification (60-80% for repeated document types). Calculate ROI: if your model costs $0.01 per inference and Redis costs $0.0001 per lookup, caching saves $0.0099 per cache hit. At 50% hit rate with 1 million daily predictions, that's $4,950 monthly savings. Implement caching when projected savings exceed Redis infrastructure costs (typically $100-500/month).

Implement three cache invalidation strategies: time-based TTL (set expiration based on how quickly predictions become stale: 5 minutes for real-time personalization, 1 hour for product recommendations, 24 hours for content classification), model-version-based invalidation (include model version in cache keys so deploying a new model version automatically serves fresh predictions without explicit cache clearing), and event-driven invalidation (clear specific cache entries when underlying entity data changes, e.g., invalidate user recommendation cache when the user makes a purchase). Use cache key design that captures all prediction-relevant inputs: hash of feature vector plus model version identifier. Monitor cache staleness by sampling cached predictions and comparing against fresh model outputs, alerting if divergence exceeds 5%. Implement a cache warming strategy for new model deployments, pre-computing predictions for the most common inputs.

Caching is valuable when three conditions are met: input patterns repeat frequently (same users, products, or queries appearing within cache TTL windows), predictions are deterministic for given inputs (same input always produces the same output), and model inference is expensive relative to cache lookup (GPU inference costs significantly more than Redis memory). Expected cache hit rates vary by application: search ranking (40-60% hit rate due to repeated popular queries), recommendation systems (20-40% for returning users), and document classification (60-80% for repeated document types). Calculate ROI: if your model costs $0.01 per inference and Redis costs $0.0001 per lookup, caching saves $0.0099 per cache hit. At 50% hit rate with 1 million daily predictions, that's $4,950 monthly savings. Implement caching when projected savings exceed Redis infrastructure costs (typically $100-500/month).

Implement three cache invalidation strategies: time-based TTL (set expiration based on how quickly predictions become stale: 5 minutes for real-time personalization, 1 hour for product recommendations, 24 hours for content classification), model-version-based invalidation (include model version in cache keys so deploying a new model version automatically serves fresh predictions without explicit cache clearing), and event-driven invalidation (clear specific cache entries when underlying entity data changes, e.g., invalidate user recommendation cache when the user makes a purchase). Use cache key design that captures all prediction-relevant inputs: hash of feature vector plus model version identifier. Monitor cache staleness by sampling cached predictions and comparing against fresh model outputs, alerting if divergence exceeds 5%. Implement a cache warming strategy for new model deployments, pre-computing predictions for the most common inputs.

Caching is valuable when three conditions are met: input patterns repeat frequently (same users, products, or queries appearing within cache TTL windows), predictions are deterministic for given inputs (same input always produces the same output), and model inference is expensive relative to cache lookup (GPU inference costs significantly more than Redis memory). Expected cache hit rates vary by application: search ranking (40-60% hit rate due to repeated popular queries), recommendation systems (20-40% for returning users), and document classification (60-80% for repeated document types). Calculate ROI: if your model costs $0.01 per inference and Redis costs $0.0001 per lookup, caching saves $0.0099 per cache hit. At 50% hit rate with 1 million daily predictions, that's $4,950 monthly savings. Implement caching when projected savings exceed Redis infrastructure costs (typically $100-500/month).

Implement three cache invalidation strategies: time-based TTL (set expiration based on how quickly predictions become stale: 5 minutes for real-time personalization, 1 hour for product recommendations, 24 hours for content classification), model-version-based invalidation (include model version in cache keys so deploying a new model version automatically serves fresh predictions without explicit cache clearing), and event-driven invalidation (clear specific cache entries when underlying entity data changes, e.g., invalidate user recommendation cache when the user makes a purchase). Use cache key design that captures all prediction-relevant inputs: hash of feature vector plus model version identifier. Monitor cache staleness by sampling cached predictions and comparing against fresh model outputs, alerting if divergence exceeds 5%. Implement a cache warming strategy for new model deployments, pre-computing predictions for the most common inputs.