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Serving predictions in real time for interactive applications.
DESCRIPTION
Online inference exposes models via APIs for low-latency, per-request predictions. It supports personalization, fraud detection, and conversational systems where milliseconds matter. Robust setups include autoscaling, caching, feature retrieval, and circuit breakers. Continuous monitoring tracks latency, error rates, and prediction quality. Safe rollout uses canary releasing, A/B testing, and rollback strategies.
RELATED TERMS
Real-time Prediction Low Latency Model Serving Autoscaling REST API A/B Testing Feature Store Canary Release
COMPETING TERMS
RELATIONS
- Broader: Inference
KNOWLEDGE AREAS
MLOps Real-time & Streaming Data Infrastructure Data Engineering
TARGET AUDIENCE
AI Engineer MLOps Engineer DevOps Engineer Software Developer
EXTERNAL SOURCES
- Wikipedia - Real-time computing
- AWS - Amazon SageMaker real-time endpoints
- Google Cloud - Vertex AI online prediction guide
- KServe - Kubernetes-native model serving framework
TOP QUESTIONS
When should I use Online Inference instead of batch?
Use online inference when user interactions or decisions require real-time responses. It is essential where latency and personalization are critical.
Category: Best practices
How do I achieve low latency in Online Inference?
Use optimized runtimes, model compression, vectorized preprocessing, in-memory feature stores, and autoscaling. Place endpoints close to users with content delivery or edge options.
Category: Performance
How do I safely deploy model updates in Online Inference?
Use canary releases or shadow deployments, monitor real-time metrics, and rollback on regressions. Version artifacts and maintain backward-compatible contracts.
Category: Deployment
How is monitoring handled in Online Inference?
Track latency percentiles, error rates, throughput, and prediction drift. Define SLIs/SLOs and alerts to ensure reliability and quality.
Category: Observability
What are common failure modes in Online Inference?
Feature fetch latency, model cold starts, and upstream dependency failures cause timeouts. Circuit breakers, caching, and warm pools mitigate issues.
Category: Risks
How does Online Inference interact with a feature store?
It fetches real-time features or joins with historical features precomputed in batch. Consistency requires careful schema and version management.
Category: Data
Can Online Inference support complex ensembles?
Yes, but beware of latency. Use efficient ensembling, distillation, or caching to keep tail latency within SLOs.
Category: Performance
How do I test Online Inference endpoints?
Use contract tests, load tests, and replay traffic. Validate predictions against canary or shadow models before full rollout.
Category: Testing
What security considerations apply to Online Inference?
Enforce authentication, rate limits, input validation, and data encryption. Monitor for adversarial inputs and abuse patterns.
Category: Security