Five shifts shaping enterprise AI platforms
Plan enterprise AI platforms around measurable outcomes, heterogeneous compute, durable agents, cost per successful task, and continuous governance.
MLOpsInfrastructureStrategy
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Practical guides to building, running, and improving ML and AI in production.
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Monitor Kubernetes ML workloads with Prometheus
Build a useful Prometheus monitoring model across Kubernetes objects, nodes, containers, applications, and ML operations without uncontrolled cardinality.
KubernetesMonitoringMLOps
Use eBPF to improve Kubernetes monitoring
Understand where eBPF adds kernel-level visibility in Kubernetes, which questions it can answer, and how to operate it safely for ML workloads.
KubernetesMonitoringObservability
Multi-architecture Docker images for ML workloads
How multi-architecture Docker images help ML teams run the same workload across developer laptops, CI, and mixed cloud compute.
DockerGuides
8 tips for Kubernetes in production
Production Kubernetes needs monitoring, RBAC, resource controls, versioned manifests, and operational discipline. This guide covers the checks that matter.
KubernetesGuides
Kubernetes operators: a complete guide
Learn how Kubernetes operators extend the API to manage application lifecycle, stateful systems, upgrades, backups, and automation.
KubernetesGuides
Kubernetes monitoring: a complete developer guide
A practical guide to Kubernetes monitoring layers, useful metrics, workload visibility, and the limits of raw cluster telemetry.
KubernetesGuides
How to use the NGINX Prometheus exporter
Connect NGINX metrics to Prometheus with the NGINX Prometheus exporter and configure scraping for basic service monitoring.
KubernetesMonitoring
Using kubectl delete
Use kubectl delete safely for Kubernetes resources, files, labels, namespaces, force deletion, and cleanup workflows.
KubernetesGuides
Prometheus exporters: tutorial and best practices
Learn how Prometheus exporters expose third-party metrics, how to build a simple exporter, and what practices keep metrics useful.
KubernetesMonitoring
Prometheus metrics: types, capabilities, and best practices
Understand Prometheus metric types, collection architecture, storage behavior, custom metrics, and Kubernetes monitoring use cases.
KubernetesMonitoring
How to use Kubernetes metrics
Find Kubernetes CPU, memory, object-state, and control plane metrics, and read them correctly with kubectl and your monitoring tools.
KubernetesGuides
Correct evaluation labels and rescore saved predictions
Keep corrected labels as a new dataset version, reuse predictions when model inputs are unchanged, and compare evaluation runs in Polyaxon.
DataOpsMLOpsLineage
Rotate workload TLS with Kubernetes Pod certificates
Use Pod certificate projections and trust bundles for workload TLS, with signer prerequisites and rotation handling for long-running ML services.
KubernetesSecurityReliability
Pull private images with Polyaxon workload service accounts
Attach a private-registry pull secret to a Kubernetes ServiceAccount, select it for a Polyaxon operation, and verify the new Pod can use the intended image.
ProductKubernetesSecurity
Build reproducible training snapshots from Kafka
Define a bounded Kafka record window, preserve its output and offset manifest, and run the snapshot job on Polyaxon.
DataOpsKafkaMLOps
Evaluate a SageMaker endpoint from Polyaxon
Call an existing SageMaker endpoint from a Polyaxon job and retain model-specific predictions and evaluation results.
MLOpsSagemakerEvaluation
Hand off Polyaxon model artifacts to KServe
Move an approved model artifact from a Polyaxon run to a separately managed KServe InferenceService.
MLOpsKServeModel Serving
Investigate Polyaxon workload health with Prometheus
Correlate Kubernetes object state, container usage, and node health when investigating a Polyaxon workload.
KubernetesPrometheusMonitoring
Monitor Polyaxon services with Prometheus
Instrument application request metrics in a Polyaxon service and configure a separate Prometheus collector to scrape them.
KubernetesPrometheusMonitoring
Optimize multi-cloud costs for ML workloads
Control multi-cloud ML spending with normalized allocation, workload unit economics, placement policies, elastic capacity, and reproducible efficiency measurements.
FinopsCost OptimizationMLOps
Run an existing TFX pipeline in a Polyaxon job
Wrap a local TensorFlow Extended pipeline in one Polyaxon job while preserving its own metadata and artifacts.
MLOpsTensorFlowPipelines
Serve a TensorFlow SavedModel with Polyaxon
Package a versioned SavedModel with TensorFlow Serving and run its REST endpoint as a Polyaxon service.
MLOpsTensorFlowModel Serving
Submit a Polyaxon workflow from AWS Lambda
Hand a trusted operation from a short AWS Lambda invocation to Polyaxon while accounting for retries and uncertain responses.
MLOpsAWSAutomation