Polyaxon v3 is coming →

Polyaxon Explore

Put your GPUs to work. Build AI that delivers.

Practical learning paths for GPU infrastructure, reproducible ML, sandboxes, and production AI.

GPU orchestration

What is GPU orchestration?

Understand how orchestration connects workflows, queues, and resource placement.

MLOps & tracking

Experiment tracking in machine learning

Record and compare parameters, metrics, and outputs.

Sandboxes

Start a sandbox

Create a sandbox-enabled run and connect with Python or the CLI.

LLM evaluation

How to evaluate RAG systems

Measure retrieval and generation separately, then assess the complete task.

GPU orchestration

Turn GPU capacity into completed work.

Learn how queues, placement, and workload recovery improve useful throughput.

Explore GPU orchestration

What is GPU orchestration?

Understand how orchestration connects workflows, queues, and resource placement.

Queue management for machine learning workloads

Set priorities and concurrency for shared infrastructure.

Gang scheduling for distributed training

Coordinate worker admission for distributed training.

How to improve GPU utilization

Find queue delays, input bottlenecks, and fragmented capacity.

MLOps & tracking

Make model development reproducible.

Learn the foundations of reproducible experiments and shared ML metadata.

Explore MLOps & tracking

What is MLOps?

Understand operations across the model development lifecycle.

MLOps core principles

Connect collaboration, automation, version control, and monitoring.

Experiment tracking in machine learning

Record and compare parameters, metrics, and outputs.

What is a metadata store for machine learning

Preserve the context and lineage behind your results.

Sandboxes

Develop in the environment where your code runs.

Understand execution boundaries, then connect your tools and automate sandbox operations.

Explore Sandboxes

What is an AI sandbox? Development environments and execution isolation

Distinguish interactive environments from runtime isolation and define their access.

Start a sandbox

Create a sandbox-enabled run and connect with Python or the CLI.

Connect with SSH

Connect local terminals and development tools to your run.

Automate with Python and CLI

Automate process and file operations from Python or the CLI.

LLM evaluation

Decide whether an AI change is ready to ship.

Choose useful metrics and evaluators, then connect release checks to production behavior.

Explore LLM evaluation

How to evaluate RAG systems

Measure retrieval and generation separately, then assess the complete task.

How to evaluate AI agents

Evaluate task outcomes, tool use, trajectories, latency, and cost.

LLM-as-a-judge: Design and validate model-based evaluators

Design rubrics and validate model-based scores against human decisions.

Offline vs. online evaluation for generative AI

Connect reproducible release checks to production feedback.

Agents

Understand the outcome and the path an agent takes.

Follow tasks across models, tools, and handoffs, and measure whether they succeed.

Explore Agents

The AI agent lifecycle

Explore the development, evaluation, and observation loop.

How to evaluate AI agents

Define success for outcomes, actions, and efficiency.

AI agent tracing: How to debug tools, loops, and handoffs

Follow model calls, tool use, loops, and handoffs.

MCP observability: Monitor tools, resources, and context

Inspect context and operations across MCP clients and servers.

Kubernetes for AI

Build the foundation for reliable AI workloads.

Learn the cluster concepts and diagnostic tools that make daily operations easier.

Explore Kubernetes for AI

Kubernetes architecture for ML workloads

Understand control planes, workers, services, and storage.

Kubernetes storage classes overview

Learn dynamic provisioning, volume binding, and persistent claims.

How to leverage Kubernetes metrics

Choose signals for resource usage, cluster state, and debugging.

How to use kubectl describe

Investigate resource state, configuration, and events.

AI observability

Understand what your AI system is doing.

Move from core signals to agent traces, MCP boundaries, and regression tests.

Explore AI observability

What is AI observability?

Connect traces, metrics, evaluations, and runtime context.

AI agent tracing: How to debug tools, loops, and handoffs

Debug tool calls, loops, and handoffs.

MCP observability: Monitor tools, resources, and context

Follow tools, resources, and context across clients and servers.

Turn production traces into regression tests

Turn representative failures into reproducible evaluation cases.

LLMOps

Operate LLM applications beyond the prototype.

Connect prompt versions, gateway decisions, and cost to the full application lifecycle.

Explore LLMOps

What is LLMOps? From prototype to production

Understand development, evaluation, deployment, and feedback.

Prompt versioning for production AI systems

Treat prompts as versioned artifacts with evaluation and rollback.

What is an AI gateway?

Understand routing, resilience, policy, and model access.

LLM cost monitoring: Measure cost per successful task

Relate model and tool costs to successful task outcomes.

AI red teaming

Find failure boundaries before they reach users.

Start with a scoped test plan, capture evidence, and verify that fixes hold.

Explore AI red teaming

What is AI red teaming?

Define test boundaries, adversarial cases, and evidence for remediation.

How to test prompt injection in LLM applications

Build direct and indirect injection cases and inspect actual tool actions.

How to red team AI agents

Test permissions, memory, handoffs, retries, and execution budgets.

Continuous AI red teaming in CI/CD

Preserve reviewed findings as complete, repeatable release checks.

Pipelines & automation

Turn repeatable steps into reliable workflows.

Move from one operation to a workflow that can run, recover, and notify consistently.

Explore Pipelines & automation

Pipelines and orchestration

Understand pipelines, orchestration, and recurring workflows.

Automation with DAGs

Express dependencies and control execution with a DAG.

Running on schedule

Run components and workflows on a recurring schedule.

Executing Hooks

Trigger follow-up operations when a run finishes.

Model registry & lineage

Connect every model version to its evidence.

Organize reusable assets, manage model versions, and follow their lineage.

Explore Model registry & lineage

Registries and reusable assets

Understand how Polyaxon organizes reusable ML assets.

Model Registry

Organize model assets and their versions.

Model versions

Associate model versions with the runs that produced them.

Run Lineage

Inspect the input and output dependencies of a run.