ML Pipeline Tracing Intermediate

Distributed tracing gives you end-to-end visibility into how data flows through your ML pipeline. By instrumenting each stage with OpenTelemetry spans, you can see exactly how long each step takes, where bottlenecks occur, and where failures originate—even when your pipeline spans multiple services and clusters.

OpenTelemetry for ML Pipelines

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider

tracer = trace.get_tracer("ml-pipeline")

def run_pipeline(data_path):
    with tracer.start_as_current_span("pipeline-run") as span:
        span.set_attribute("data.path", data_path)

        with tracer.start_as_current_span("data-ingestion"):
            data = ingest_data(data_path)

        with tracer.start_as_current_span("feature-engineering"):
            features = engineer_features(data)

        with tracer.start_as_current_span("model-training") as train_span:
            model = train_model(features)
            train_span.set_attribute("model.accuracy", model.accuracy)

Key Spans for ML Pipelines

• Data ingestion — Track records read, data source, file sizes, and parsing errors
• Feature engineering — Track feature computation time, feature count, and transformation errors
• Training — Track epochs, loss values, learning rate, and GPU utilization during training
• Evaluation — Track model metrics (accuracy, F1, AUC) and comparison with baseline
• Model registration — Track model artifact size, registry upload time, and version assignment
• Deployment — Track rollout strategy, canary percentage, and health check results

Trace Context Propagation

When ML pipeline stages run as separate services or Kubernetes jobs, trace context must be propagated between them. Use W3C Trace Context headers or pass trace IDs through message queues and job metadata.

Visualizing Traces with Jaeger

Jaeger provides a timeline view of your pipeline execution, showing:

• Waterfall view — See all spans in chronological order with parent-child relationships
• Critical path analysis — Identify which stage is the bottleneck in your pipeline
• Comparison view — Compare traces from different pipeline runs to spot regressions
• Service dependency map — Visualize how pipeline components communicate