Add Polyaxon to your code with just a few lines, and we'll automatically capture and log system metrics for you.

• Enable hyperparams and metrics logging for performance measurement
• Data, artifacts, and outputs logging for pipelines and reproducible experiments
• Centralize your runs’ logs

• Search and get better understanding of your experiments performance
• Enable access to multiple dashboarding and vizualuasation tools

Link between code and model in very easy without altering your workflow

• Leverage advance scheduling schemas without wasting time on devops
• Schedule parallel and distributed runs for rapid iterations
• Maximize utilization of computing resources to scale the volume, variety, and complexity of models in development and production

• Grow and shrink compute resources elastically as needed
• Add GPUs and resources to your cluster as needed

• Train experiments on the cloud, on premise, or local machines and keep track of results and artifacts
• Manage all resources and workflows in a single interface

• Polyaxon knows how to schedule an experiment to run with the same conjugation and parameters to reach the same results
• Automate repeatable tasks that do not benefit from domain expertise so your experts focus their effort where it will have the greatest impact

• Use various visualizations and graphs to derive insights from the experiments
• Get accurate information about the importance of your parameters

Compare experiment results, hyperparams, versions of training data and source code, so you can quickly analyze what worked and what didn’t

Automatically discovers and documents the success metrics, hyperparams, source code, data, visualizations, artifacts and resources used in each experiment

See the full experiment history at a glance, including when, who and where

you can easily start various tools to enable data explorations and metrics visualizations

• View other teammates’ experiments
• Have enterprise-grade access control, team management, and usage monitoring features for data science managers and executives.

• Perform model selection and architecture search to transform the model development process
• Develop high-performing, differentiated models at a faster rate to amplify the impact of these models on your business

• Automate your model tuning and parameters search
• Use hyperparameter optimization to schedule large number of experiments

Augment your data scientists with solutions to produce better models

• Leverage your compute resources and tooling to give data scientists the agility they need to develop and deploy innovative models
• Model development allows data scientists to rapidly develop models, experiment collaboratively, and extract intuitive insights
• Deploy models to Production in a rigorous way to have impact on real business decisions and drive real value

• Enhance your Model Governance by monitoring and maximising the performance of your models
• Establish rigorous model CI/CD to centralize knowledge, insights, and artifacts generated while building or using the models
• Find, reuse, and build upon to drive rapid innovation