Edge ML Deployment
Deploying ML models to smartphones, IoT devices, and embedded systems - model compression, edge runtimes, OTA updates, federated learning, and real-world examples.
5 docs tagged with "real-time-ml"
View all tagsEvent-Driven ML Architecture
Designing ML systems around events - event sourcing, CQRS for feature stores, the outbox pattern, and how LinkedIn's unified messaging platform drives ML at scale.
Low-Latency Inference Patterns
Engineering ML predictions under 10ms p99 - hardware choices, model optimization, batching strategies, pre-computation, memory layout, and real production targets.
Real-Time Feature Engineering at Scale
Computing ML features from raw events within milliseconds - Redis patterns, sliding window aggregations, session detection, and Uber's Michelangelo real-time pipeline.
Stream Processing for ML Systems
Continuous feature computation on unbounded data streams using Apache Flink - windowing, watermarks, state management, and production ML feature pipelines.