MLOps Guides and Experiment Tracking

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.

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.

Version the dataset behind every evaluation

Use Polyaxon data references, artifact logging, and registered versions to retain the dataset and split behind each evaluation.

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.

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.

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.

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.

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.

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.

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.

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.

Secure enterprise AI from data to deployment

Apply security controls across data, training, evaluation, artifacts, deployment, and operation without slowing every AI workload equally.

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.

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.

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.

From notebooks to repeatable ML jobs

Move notebook experiments into repeatable ML jobs with explicit inputs, versioned code, reproducible containers, and Polyaxon tracking.

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.

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.

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.

What is LLMOps? From prototype to production

LLMOps applies repeatable development, evaluation, deployment, and observability practices to production LLM applications and AI agents.

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.

What is AI observability?

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

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.

Make a Kubernetes platform ready for AI workloads

Assess and close the gaps in accelerator access, batch scheduling, inference, data, identity, observability, cost, and ownership before AI workloads scale on Kubernetes.