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Connect your local tools to Polyaxon with SSH

Connect native terminals and IDEs to a Polyaxon service, forward application ports, and use tmux explicitly when you want to resume a shell.

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.

Reconnect to your Polyaxon shell

Learn how ordinary ops shells differ from tmux sessions, then detach and reconnect to the same Polyaxon shell from the CLI or UI.

Optimize LLM inference with repeatable benchmarks

Compare inference optimizations against a fixed workload, latency limits, and quality checks, with benchmark configurations and results tracked in Polyaxon.

Combine run filters with AND and OR

Use OR to combine metric thresholds, negated conditions, and independent groups of filters in Polyaxon queries.

Tune LLM hyperparameters within a compute budget

Design a small LoRA fine-tuning sweep, control its execution in Polyaxon, and compare validation quality, resource use, and repeatability.

Write shorter Polyaxonfiles

Use direct parameter values and let input and output defaults imply optionality, with a look ahead at simpler workload definitions planned for Polyaxon v3.

Connect LLM production traces to evaluation runs

Keep release, dataset, evaluator, and sampling context connected as you investigate LLM failures and compare fixes with Polyaxon.

Route ML jobs to regional infrastructure with Polyaxon

Configure regional execution with Polyaxon agents, queues, and storage connections, then measure data-transfer costs without assuming same-region traffic is free.

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.

Upload multiple files and folders with Polyaxon CLI

Upload code, configuration, and small datasets in one Polyaxon CLI command, then save the same path mappings in a declarative mount section.

Reduce the cost of self-hosted ML workloads

Reduce self-hosted ML costs with outcome-based accounting, right-sized resources, elastic capacity, interruption-ready workloads, local data paths, and deliberate retention.

Design multi-tenant AI agent infrastructure on Kubernetes

Separate identity, network access, data, queues, budgets, and evidence when multiple teams or customers run AI agents on shared Kubernetes infrastructure.

Sandbox AI agents on Kubernetes

Design isolated Kubernetes execution for AI agents with a threat model, stronger runtimes, scoped identity, controlled egress, bounded storage, and auditable cleanup.

What is sovereign AI? Control across the AI lifecycle

Define sovereign AI as control over data, models, compute, operations, providers, and evidence, then implement it with Kubernetes.

Fine-tune Mistral 7B with LoRA on Kubernetes

Plan a Mistral 7B LoRA fine-tuning workflow on Kubernetes with versioned data, GPU scheduling, Polyaxon tracking, evaluation, and adapter packaging.

Run Promptfoo evaluations on Kubernetes with Polyaxon

Package a Promptfoo suite as a Polyaxon job, export evaluation reports, track artifacts, and verify that failed checks fail the workload.

What are your ML jobs connecting to?

Trace image pulls, Git clones, S3 and GCS access, Hugging Face downloads, and artifact uploads across the lifecycle of Kubernetes jobs and sandboxes.

Pods, Jobs, or Services for AI agents?

Choose the Kubernetes execution shape for an AI agent by separating logical task identity from Pods, Jobs, Services, workflows, and sandbox lifecycles.

Orchestrate parallel coding agents with Polyaxon

Run parallel coding agents in separate Polyaxon workspaces, collect candidate patches, evaluate independently, and merge only reviewed changes.

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.

Govern model access with an AI gateway

Operate an AI gateway as a policy boundary for workload identity, eligible models, routing, budgets, privacy, evidence, and controlled configuration changes.

Kubernetes for AI agents: A platform engineering guide

Design a Kubernetes platform for AI agents with clear workload boundaries, durable state, scoped access, resource controls, and end-to-end evidence.

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.