Polyaxon Features

Build, monitor, deliver and scale machine learning experiments — we're the easiest way to go from an idea to a fully deployable model, bypassing all those infrastructure headaches.

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A Platform for reproducible and scalable Machine Learning and Deep Learning applications.

Learn more about the suite of features and products that underpin today's most innovative platform for managing data science workflows.

Gain more productivity and ship faster

Polyaxon provides an interactive workspace with notebooks, tensorboards, visualizations,and dashboards.

User management

Collaborate with the rest of your team, share and compare experiments and results.

User resources allocation

Manage your team's resources and parallelism, and set quotas.

Versioning and reproducibility

Reproducible results with a built-in version control for code and experiments.

Data autonomy

Maintain complete control of your data and persistence choices.

Hyperparameter search & optimization

Run group of experiments in parallel and in distributed way.

Maximizes Resource Utilization

Spin up or down, add more nodes, add more GPUs, and expand storage.

Cost Effective

Leverage commodity on-premise infrastructure or spot instances to reduce costs.

Powerful interface

Author jobs, experiments, and pipelines in Json, YAML, and Python.

Runs on any infrastructure

Deploy Polyaxon in the cloud, on-premises or in hybrid environments, including single laptop, container management platforms, or on Kubernetes.


Polyaxon can easily scale horizontally by adding more nodes.


Extend Polyaxon's functionalities with custom plugins.


Stream, filter, and search logs from all operations.


Enforce SLAs with TTL, timeout, and retries.

Artifacts and Lineage

Track artifacts and lineage information.

Custom Run Environments

Configure reusable presets or define per run environments.


Send and subscribe to events mid-run or at the end of operations.

Rendering and Visualization

Log artifacts and custom visualizations.

Manual Triggers

Start, stop, resume, restart, and copy any job or service.


Organize your efforts and define directories of work.

Component Hub

Extract reusable modules and define typed inputs and outputs for your runtime.

Model Registry

Lock experiments and promote your work for production deployment.

Powerful Search

Search by name, description, regex, specific fields, metrics, or configurations.

Cache Layer

Avoid running expensive computations several times.

Affinity and Routing

Runs are assigned to queues and they are only picked up by agents with matching tags, you can manage multiple namespaces and clusters.


Parallelize your work with operation mapping and hyperparameter optimization.


Filter, aggregate, and annotate inputs/outputs/artifacts from multiple or parallel upstream runs.


Define the success of your workflows based on early stopping strategies.


Enforce global concurrency, and split the quota on per queue or workflow level.


Define executions priority and enforce rate limits.


Every run is versioned and has a unique hash, all outputs and artifacts are tracked.

Auto API, authZ, and authN

Every run will be checked prior to execution and will have a permissioned API.

Team Management

Invite users to your team and assign roles and permissions as appropriate.


Integrate with external systems with hooks and actions.

Versatile Runtime

Native Kubernetes, Kubeflow operators, Ray/Dask/Spark operators, custom integrations, ...


Our open-source component to enable hybrid, multi-namespace, and multi-cluster deployments.

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