Observability for machine learning
ML observability connects logs, metrics, artifacts, infrastructure signals, and model behavior so teams can debug training and serving systems.
Observability usually starts as an infrastructure word. Logs, metrics, traces, dashboards, alerts. For machine learning, that definition is too narrow.
ML systems need infrastructure observability, but they also need run context: which code ran, which data version was used, which parameters changed, which artifact was produced, which GPU was idle, and which model version is now serving traffic.
Training pipeline observability
Training pipelines interact with data warehouses, object stores, feature pipelines, GPUs, distributed workers, and model registries. A failure can come from application code, data quality, credentials, resource limits, networking, image builds, or a flaky dependency.
Good observability gives teams the shortest path from symptom to cause.
Logs
Logs are still the first debugging tool. Python libraries, data loaders, training frameworks, and application code all emit information that can explain a failure. In Kubernetes, those logs live with short-lived pods, so they need to be captured and attached to the run before the pod disappears.
For ML teams, logs are most useful when they are tied to run metadata. A raw pod name is less helpful than a run, project, component, queue, commit, and artifact lineage.
Failures and exceptions
Long-running pipelines fail. Memory limits, missing files, bad parameters, package conflicts, unavailable services, and data assumptions all show up eventually. Teams need direct access to failure state, exit codes, traceback output, and the workload definition that produced the failure.
Resource usage
ML workloads can waste expensive compute quietly. GPUs sit idle during data bottlenecks. CPU jobs request too much memory. Distributed workers wait for slow storage. Without resource visibility, teams optimize model code while the real problem sits in I/O or scheduling.
Useful signals include CPU, memory, GPU utilization, network throughput, ephemeral storage, queue wait time, and runtime duration.
Follow one slow training run
Suppose a training run takes longer after a dataset refresh and its GPU is intermittently idle. Low utilization is a symptom, not proof that the GPU allocation is too large. Work through the timeline:
| Evidence | Question to investigate | Next source to inspect |
|---|---|---|
| Long delay before the pod runs | Is the workload waiting for admission or eligible capacity? | Run statuses, pod conditions, and queue configuration |
| Long startup before the first batch | Is image download, initialization, or model loading responsible? | Init-container and application logs |
| Gaps between batches while the GPU is idle | Is data loading, preprocessing, synchronization, or storage delaying work? | Application timings, worker logs, and storage measurements |
| Training ends but completion is delayed | Are checkpoints or evaluation artifacts still being written? | Output-stage logs and storage errors |
Use the run resources dashboard for available run-level signals and the platform observability setup for the surrounding services. Application-stage timings require instrumentation in the training code; a resource chart cannot identify every internal stage.
Compare the slow run with a baseline using the same model, allocation, and intended data contract. If the data revision changed, preserve both manifests and inspect changed file counts, sizes, and preprocessing work. Form one hypothesis—for example, many small remote reads are stalling the loader—then change one relevant factor and compare again. Keep the evidence that supported or rejected the hypothesis with the run.
Model and data behavior
Infrastructure health does not prove model health. Teams also need metrics, evaluation artifacts, prediction samples, drift checks, data summaries, and lineage. A model can run perfectly and still be wrong.
The model-behavior walkthrough shows how an aggregate improvement can hide a failing cohort. Combine those evaluation artifacts with runtime evidence so a model-quality regression and a throughput regression can be investigated separately.
How Polyaxon helps
Polyaxon connects workload execution with ML metadata. Runs carry logs, statuses, parameters, artifacts, lineage, and runtime context in one place. That makes debugging less dependent on stitching together Kubernetes commands, object storage paths, and screenshots from separate systems.
Use artifact lineage to identify the input snapshot and resulting model, and metadata logging for application-specific measurements. The practical outcome is a reproducible explanation: which evidence changed, which cause was investigated, and whether the next run improved the original symptom without harming model behavior.