Experiments (Preview)

This chapter provides information on creating, managing, and developing experiments in your workspace.

Experiments in Oracle AI Data Platform Workbench provide means for data scientists, machine-learning engineers, and managers to collaborate in the development of models. AI Data Platform Workbench enables you to:

• Track experiments for building best models via performance analysis, collaboration, analysis of experimental conditions (hyper-parameters, input dataset, feature engineering etc.)
• Ensure old experiments aren’t repeated with same experimental conditions
• Rerun old experiments with different experimental conditions for likely performance gains
• Reproducibility of earlier experiments
• Facilitate retraining with newer datasets when performance degrades or retraining is warranted
• Load a model from Model Registry into notebook and compare model performance versus a new model being developed

Note:

If you haven't used previously used Experiments or Models in your AI Data Platform Workbench, you need to either restart your associated compute cluster or create a new one to use with Experiments and Models.

Limitations

Experiments are currently not supported by ARM-based compute clusters. Ensure the attached compute cluster is either Intel- or AMD-based.