What is Prediction Serving?

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

Start with a REST API using FastAPI or Flask wrapping your model inference code, containerized with Docker, and deployed to a managed service like Cloud Run, ECS, or Kubernetes. This handles 100-10,000 requests per second for most models. Add a load balancer, health checks, and basic monitoring. Total setup time: 1-2 days. Avoid building custom serving infrastructure until you outgrow managed services. For batch predictions, a scheduled job processing files is even simpler.

Use real-time serving when predictions are needed at the moment of user interaction: search rankings, fraud detection, chatbot responses, and recommendation requests. Use batch serving when predictions can be precomputed: daily risk scores, weekly churn predictions, nightly content recommendations. Batch serving is 3-5x cheaper per prediction. Many systems combine both: batch-compute common predictions and fall back to real-time for uncommon requests. Choose based on freshness requirements, not technical preference.

Implement fallback strategies at multiple levels. Cache recent predictions for repeat requests. Maintain a simpler fallback model that's more robust. Define default predictions for complete outages based on business logic like showing popular items when the recommendation model is down. Use circuit breakers to prevent cascading failures. Return explicit uncertainty indicators rather than silently serving low-quality predictions. Monitor fallback activation rates since frequent activation signals underlying reliability issues.

Start with a REST API using FastAPI or Flask wrapping your model inference code, containerized with Docker, and deployed to a managed service like Cloud Run, ECS, or Kubernetes. This handles 100-10,000 requests per second for most models. Add a load balancer, health checks, and basic monitoring. Total setup time: 1-2 days. Avoid building custom serving infrastructure until you outgrow managed services. For batch predictions, a scheduled job processing files is even simpler.

Use real-time serving when predictions are needed at the moment of user interaction: search rankings, fraud detection, chatbot responses, and recommendation requests. Use batch serving when predictions can be precomputed: daily risk scores, weekly churn predictions, nightly content recommendations. Batch serving is 3-5x cheaper per prediction. Many systems combine both: batch-compute common predictions and fall back to real-time for uncommon requests. Choose based on freshness requirements, not technical preference.

Implement fallback strategies at multiple levels. Cache recent predictions for repeat requests. Maintain a simpler fallback model that's more robust. Define default predictions for complete outages based on business logic like showing popular items when the recommendation model is down. Use circuit breakers to prevent cascading failures. Return explicit uncertainty indicators rather than silently serving low-quality predictions. Monitor fallback activation rates since frequent activation signals underlying reliability issues.

Start with a REST API using FastAPI or Flask wrapping your model inference code, containerized with Docker, and deployed to a managed service like Cloud Run, ECS, or Kubernetes. This handles 100-10,000 requests per second for most models. Add a load balancer, health checks, and basic monitoring. Total setup time: 1-2 days. Avoid building custom serving infrastructure until you outgrow managed services. For batch predictions, a scheduled job processing files is even simpler.

Use real-time serving when predictions are needed at the moment of user interaction: search rankings, fraud detection, chatbot responses, and recommendation requests. Use batch serving when predictions can be precomputed: daily risk scores, weekly churn predictions, nightly content recommendations. Batch serving is 3-5x cheaper per prediction. Many systems combine both: batch-compute common predictions and fall back to real-time for uncommon requests. Choose based on freshness requirements, not technical preference.

Implement fallback strategies at multiple levels. Cache recent predictions for repeat requests. Maintain a simpler fallback model that's more robust. Define default predictions for complete outages based on business logic like showing popular items when the recommendation model is down. Use circuit breakers to prevent cascading failures. Return explicit uncertainty indicators rather than silently serving low-quality predictions. Monitor fallback activation rates since frequent activation signals underlying reliability issues.