Artifacts

Summary

Use the Artifacts page to share specific artifacts with team members, facilitating easier collaboration and ensuring consistency across the project.

To log a specific artifact for your training code, include the artifact path in your code like this: arcee.artifact("artifact_path").

Actions

• Update the Content: Click the Refresh button to view the latest information.

• View Artifacts: Get the artifacts list with detailed information about the artifacts of your experiments.

• Filter the data: Filter the artifacts by Tasks or Timerange.

Tips

• Replicability: Reproduce experiments and results by storing artifacts.

• Facilitate collaboration: Share artifacts like model weights, data preprocessing scripts, and evaluation metrics to help your team members work more effectively together and build upon each other's efforts.

• Expandability: Scale the training and deployment processes with large-scale machine learning systems. For instance, preprocessed datasets can be reused across different training jobs, saving time and computational resources.