Monitoring articles

Design reliable self-hosted ML infrastructure

Build self-hosted ML infrastructure around explicit failure domains, durable queues and artifacts, eligible failover, actionable telemetry, and tested recovery.

SLOs for AI applications and agents

Define service-level objectives for AI quality, task success, safety, latency, availability, and cost using measurable user-centered indicators.

GPU utilization metrics: allocation, activity, and throughput

Learn which GPU utilization metrics explain capacity, device activity, memory pressure, and useful ML throughput—and how to avoid misleading averages.

LLM cost monitoring: Measure cost per successful task

Connect tokens, model calls, retrieval, tools, retries, and infrastructure with quality and task outcomes to control production LLM costs.

Kubernetes probes for ML services

Configure startup, readiness, and liveness probes for model servers and interactive ML services without causing restart loops or hiding dependency failures.

Load-test ML services on Kubernetes

Design repeatable Kubernetes load tests for model services using realistic arrivals, tail latency, queueing, accelerator metrics, and recovery criteria.

Kubernetes startup probes for model servers

Give model servers enough time to load weights and warm runtimes without weakening liveness detection for the rest of their lifecycle.

What is AI observability?

AI observability connects traces, metrics, evaluations, feedback, and runtime context so teams can understand and improve models, applications, and agents.

Troubleshoot Kubernetes disk pressure

Diagnose Kubernetes DiskPressure, understand eviction signals, and prevent images, logs, and local ML data from exhausting node storage.

Django logging on Kubernetes

Configure Django logs for container collection, useful request context, exception diagnosis, privacy, and correlation with Kubernetes workload state.

Monitor Node.js services on Kubernetes

Monitor Node.js services with request outcomes, event-loop delay, memory, dependencies, Kubernetes state, and low-cardinality telemetry.

Python logging for ML workloads

Create useful Python logs for training and batch workloads with structured context, exception details, stdout collection, and controlled volume.

Observability for machine learning

ML observability connects logs, metrics, artifacts, infrastructure signals, and model behavior so teams can debug training and serving systems.

Use kube-state-metrics for ML platforms

Monitor Kubernetes object state for ML workloads while separating desired state, resource usage, application outcomes, and high-cardinality metadata.

Kubernetes audit logs for ML platforms

Design Kubernetes audit policy, collection, retention, and investigation workflows for shared ML clusters without recording sensitive payloads by default.

Monitor Django applications on Kubernetes

Connect Django request outcomes, database and cache health, worker behavior, Kubernetes state, and release context in one monitoring strategy.

Container logging for ML workloads

Design container logs for local Docker debugging and Kubernetes collection without losing run context, exhausting nodes, or exposing sensitive data.

OpenTelemetry Collector for ML platforms

Design OpenTelemetry Collector pipelines for ML services with clear receivers, processors, exporters, deployment patterns, and failure controls.

Monitor Amazon EKS for ML workloads

Build layered Amazon EKS monitoring for control-plane activity, Kubernetes state, nodes, GPUs, applications, ML runs, and telemetry health.

Prometheus Alertmanager for ML platforms

Design Prometheus alerts and Alertmanager routing for ML platforms without noisy pages, missing owners, or unsafe high-cardinality labels.

Monitoring vs. observability for ML systems

Separate monitoring from observability, then combine metrics, logs, traces, run context, and model evaluation into an effective ML operating model.

Managed Prometheus for ML platforms

Decide between managed and self-operated Prometheus using scale, availability, PromQL compatibility, data governance, cost, and operational ownership.

BTF for portable eBPF observability

Understand how BPF Type Format supports eBPF introspection, CO-RE portability, safer deployment, and kernel-level observability for ML infrastructure.

OpenTelemetry zero-code instrumentation

Use OpenTelemetry automatic instrumentation as a safe baseline, then add domain spans, stable attributes, sampling, and rollout controls for ML services.