What is Distributed Tracing?

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

Tracing shines in multi-service prediction pipelines where a single request touches feature retrieval, preprocessing, model inference, post-processing, and response formatting. It reveals which component causes latency spikes that aggregate metrics can't pinpoint. Tracing also shows dependency relationships between services and identifies bottlenecks during load. For ensemble models that combine multiple model outputs, tracing shows which sub-model contributes most to total latency.

OpenTelemetry is the industry standard for instrumentation, providing vendor-neutral trace collection. Jaeger and Zipkin are popular open-source backends for trace storage and visualization. For managed services, AWS X-Ray, Google Cloud Trace, and Datadog APM provide tracing with minimal setup. Instrument at service boundaries automatically using service mesh sidecars. Add manual instrumentation at ML-specific boundaries like feature retrieval and model inference to capture the most useful spans.

With sampling rates of 1-10%, tracing adds less than 1% latency overhead and minimal storage costs. Sampling is essential since tracing every request generates excessive data. Use adaptive sampling that increases rate during incidents for better diagnosis. Head-based sampling decides at request start whether to trace, keeping overhead predictable. Tail-based sampling traces based on outcome like high latency, capturing more interesting traces but requiring more infrastructure. Start with 1% head-based sampling and adjust based on your debugging needs.

Tracing shines in multi-service prediction pipelines where a single request touches feature retrieval, preprocessing, model inference, post-processing, and response formatting. It reveals which component causes latency spikes that aggregate metrics can't pinpoint. Tracing also shows dependency relationships between services and identifies bottlenecks during load. For ensemble models that combine multiple model outputs, tracing shows which sub-model contributes most to total latency.

OpenTelemetry is the industry standard for instrumentation, providing vendor-neutral trace collection. Jaeger and Zipkin are popular open-source backends for trace storage and visualization. For managed services, AWS X-Ray, Google Cloud Trace, and Datadog APM provide tracing with minimal setup. Instrument at service boundaries automatically using service mesh sidecars. Add manual instrumentation at ML-specific boundaries like feature retrieval and model inference to capture the most useful spans.

With sampling rates of 1-10%, tracing adds less than 1% latency overhead and minimal storage costs. Sampling is essential since tracing every request generates excessive data. Use adaptive sampling that increases rate during incidents for better diagnosis. Head-based sampling decides at request start whether to trace, keeping overhead predictable. Tail-based sampling traces based on outcome like high latency, capturing more interesting traces but requiring more infrastructure. Start with 1% head-based sampling and adjust based on your debugging needs.