Real-time ML Inference Systems
Build real-time ML inference systems: streaming features, low-latency serving, online learning, and production optimization strategies.
What is Real-Time ML Inference?
Real-time ML inference is the production process of turning a live request or event into a model prediction within a strict latency deadline. A complete path usually retrieves fresh features, selects a model, runs inference, applies business rules, records telemetry, and returns or emits the result.
It matters because user-facing ranking, fraud checks, routing, and recommendations can lose value in milliseconds. The system must balance tail latency, throughput, freshness, consistency, cost, and graceful degradation rather than optimize model execution in isolation.
Real-Time Inference Performance Calculator
Estimate latency, throughput, and memory for a cached inference path.
Performance Metrics
Redis Cache
- Cached latency
- 9ms
- Uncached latency
- 11ms
- Latency improvement
- 20% faster
- Effective throughput
- 3,523 RPS
- Memory usage
- 750 MB
- Cache hit rate
- 85%
- Cost efficiency
- 47/10
How does the performance estimate work?
The lesson calculates a base latency from model size, feature dimensions, and a fixed batch size of 32:
base latency = ln(model MB) x 5 + (feature dimensions / 100) x 2 + (batch size / 8) x 3- Hardware multiplier: CPU
1.0, GPU0.3, TPU0.2 - The fixed high-compression multiplier is
0.6 uncached latency = base latency x hardware multiplier x compression multipliercached latency = uncached latency x cache latency factoreffective throughput = min(requested RPS, 1,000 / cached latency x batch size)total memory = compressed model memory + cache overhead
The demo reports latency improvement, typical hit rate, and a normalized cost-efficiency score from effective throughput divided by total memory.
Which real-time architecture should you use?
Interactive
Request-response
Return one decision before the caller's deadline. Feature retrieval, queueing, inference, policy, and serialization all share that budget.
Asynchronous
Event stream
Consume durable events and emit predictions continuously. Ordering, replay, state, and idempotent outputs replace synchronous response concerns.
Bounded wait
Micro-batch
Accumulate a small number of compatible requests to improve utilization. Set a maximum wait so throughput does not consume the product deadline.
Move work earlier
Precompute or edge
Serve a recent prediction near the caller when freshness and key cardinality permit. Invalidation and versioned cache keys become correctness concerns.
How do cache strategies compare?
Fastest local read
In-process
Avoids a network hop but duplicates memory across replicas and loses entries on restart. Use only for bounded, safe-to-recompute values.
Shared regional state
Distributed memory
Shares predictions or features across servers. The cache is still a dependency with timeout, capacity, hot-key, and invalidation failure modes.
Near the caller
Edge cache
Works for broadly reusable results whose privacy, model version, policy, and freshness can be encoded in a safe key.
Personal or changing
No prediction cache
Run inference when outputs depend on private, high-cardinality, rapidly changing, or non-deterministic inputs. Optimize features and runtime instead.
The cache hit rate changes how much traffic reaches the model fleet. The example below assumes a workload whose results are safe to reuse; it is not a recommendation to cache every prediction.
How are real-time inference systems implemented?
High-throughput streaming inference
Multi-model serving with dynamic routing
Online learning with streaming updates
What do production real-time systems look like?
Fresh features
ETA or risk score
Combine live context with durable entity features, use a bounded model call, and return a fallback estimate when fresh dependencies miss the deadline.
Retrieve then rank
Recommendation
Generate a small candidate set before expensive ranking. Log exposure so clicks do not become an uncorrected feedback loop.
High-cost error
Fraud decision
Use calibrated thresholds, rules, and human review around the model. Preserve idempotency and an auditable reason path for adverse actions.
Several stages
Search ranking
Run lexical retrieval, learned retrieval, ranking, and policy under separate budgets. Degrade by skipping stages instead of timing out the request.
What are real-time ML best practices?
Do
- Use memory, Redis, and edge caches for different latency and scope needs.
- Use non-blocking I/O for feature retrieval and model serving.
- Monitor feature freshness and enforce lag SLAs.
- Add circuit breakers and graceful fallback behavior.
- Batch compatible requests when throughput benefits exceed queueing delay.
Avoid
- Ignoring model cold starts; keep warm pools for slow-loading artifacts.
- Synchronous database calls on the critical path.
- Optimizing before profiling end-to-end latency.
- Single points of failure in critical components or data.
- Operating without comprehensive latency, error, freshness, and saturation telemetry.