ML Metadata Store: Examples, Artifacts, and Lineage
Understand ML metadata with examples of runs, datasets, and model artifacts. Learn how a metadata store connects them and how to query them in Polyaxon.
MLOps
MLOps
Explore MLOps principles, experiment tracking, metadata, and reproducible machine learning workflows with practical Polyaxon guides.
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Essential guides, selected for this topic.
ML Experiment Tracking: What to Log and How to Compare Runs
Learn what to record for each ML experiment, how to compare runs fairly, and how to log parameters, metrics, datasets, and model artifacts with Polyaxon.
MLOps
Hyperparameters tuning on Kubernetes
hyperparameters tuning is very important concept in order to choose the optimal hyperparameters for a given algorithm!
ProductMLOps
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
Resume interrupted training without losing progress
Build recoverable PyTorch training jobs with complete checkpoints, durable storage, and explicit restoration. Practice recovery locally and with Polyaxon.
MLOpsKubernetesPyTorch
Latest articles
The latest guides, tutorials, and product updates.
Optimize one model three ways with NVIDIA Model Optimizer on Polyaxon
Compare INT8 quantization, FastNAS pruning, and distillation against one tracked baseline, then measure the candidate that meets your accuracy floor.
MLOpsModel OptimizationTraining
Keep failed candidates visible in pipeline reports
Reconcile evaluation results against an expected candidate manifest, preserve failed and missing outcomes, and separate pipeline reporting from release approval.
MLOpsOrchestrationEvaluation
Design a search space before launching the sweep
Use Polyaxon matrices to choose parameter scales, separate meaningful combinations, and control the size of a hyperparameter sweep.
Hyperparameter TuningMLOpsExperimentation
Version the dataset behind every evaluation
Use Polyaxon data references, artifact logging, and registered versions to retain the dataset and split behind each evaluation.
DataOpsMLOpsLLMOps
Resume interrupted training without losing progress
Build recoverable PyTorch training jobs with complete checkpoints, durable storage, and explicit restoration. Practice recovery locally and with Polyaxon.
MLOpsKubernetesPyTorch
Did you find better hyperparameters—or a lucky seed?
Confirm hyperparameter finalists across matched training seeds, compare variation and paired differences, and reserve compute for a defensible final choice.
Hyperparameter TuningMLOpsExperimentation
Prevent data leakage in ML and LLM evaluation datasets
Choose evaluation boundaries, keep related examples together, audit duplicate overlap, and retain reproducible split manifests for ML and LLM experiments.
DataOpsMLOpsLLMOps
Unify hybrid AI platform operations
Operate AI workloads across clusters and clouds with central intent, local execution, placement policy, consistent identity, connected evidence, and failure-aware control.
Hybrid CloudInfrastructureMLOps
Build an enterprise AI security operating model
Define ownership, risk tiers, platform boundaries, exceptions, and evidence so enterprise AI security operates continuously instead of as a launch checklist.
SecurityGovernanceMLOps
Scan model artifacts before adding them to a registry
Add static model artifact scanning before registry promotion, preserve scan evidence, check coverage, and bind approval to immutable artifact digests.
Model RegistrySecurityMLOps
Connect agent execution to the ML platform
Connect AI agent execution to Polyaxon projects, components, scheduling, tracking, and artifacts through a stable application-owned execution contract.
AI AgentsMLOpsArchitecture
ML infrastructure explained for business teams
Understand what ML infrastructure pays for, how it affects delivery and reliability, and how to evaluate an investment using measurable workflow outcomes.
MLOpsInfrastructureGuides
Secure enterprise AI from data to deployment
Apply security controls across data, training, evaluation, artifacts, deployment, and operation without slowing every AI workload equally.
SecurityGovernanceMLOps
Production LLM systems: Where to invest after the prototype
Use Polyaxon run tracking, comparison dashboards, resource monitoring, and repeatable evaluation to decide what to improve after an LLM prototype.
LLMOpsMLOpsGuides
Move faster with risk-tiered AI delivery
Use consequence-based AI risk tiers to apply proportionate data, evaluation, security, approval, deployment, monitoring, and incident controls.
GovernanceSecurityMLOps
Design open infrastructure for portable AI workloads
Keep AI workloads portable with explicit execution contracts, open packaging and telemetry, hardware abstraction, data boundaries, and tested migration paths.
InfrastructureKubernetesMLOps
From notebooks to repeatable ML jobs
Move notebook experiments into repeatable ML jobs with explicit inputs, versioned code, reproducible containers, and Polyaxon tracking.
MLOpsGuides
Extend your MLOps workflow to AI agent development
Package an agent evaluator as a Polyaxon component, track candidate revisions and task metrics, and compare changes using existing MLOps workflows.
AI AgentsMLOpsLLMOps
Build an ML knowledge repository your team can reuse
Connect experiment records, dataset versions, model artifacts, and review decisions into a reusable ML knowledge repository with Polyaxon.
MLOpsModel RegistryGuides
Build golden paths for enterprise AI delivery
Create self-service AI delivery paths with explicit workload contracts, reusable components, governed connections, evaluation gates, evidence, and safe exceptions.
Platform EngineeringMLOpsGovernance
What is LLMOps? From prototype to production
LLMOps applies repeatable development, evaluation, deployment, and observability practices to production LLM applications and AI agents.
LLMOpsMLOps
What is distributed learning?
Distributed learning splits model training across processors or machines. Learn the main strategies, tradeoffs, and how to run it on Kubernetes.
MLOpsGuides
What is AI observability?
AI observability connects traces, metrics, evaluations, feedback, and runtime context so teams can understand and improve models, applications, and agents.
MLOpsMonitoring
Microservices on Kubernetes for ML platforms
Choose service boundaries for Kubernetes-based ML platforms without turning every component, model, or workflow step into a separate microservice.
KubernetesMLOpsGuides