Manage versioned ML evaluation datasets in Arize via the ax CLI.
npx skills add https://github.com/ethan-rio/skills --skill arize-dataset-ethan-rio
Please help me install this Agent Skill. Skill: arize-dataset Source: https://github.com/ethan-rio/skills/tree/main/skills/arize/arize-dataset Command: npx skills add https://github.com/ethan-rio/skills --skill arize-dataset-ethan-rio
Manually creating, versioning, and managing ML evaluation datasets in Arize is time-consuming and prone to errors like field mismatches or truncated exports, this skill automates those workflows using the official ax CLI.
Use the arize-dataset skill to create a new evaluation dataset named 'customer-support-eval' from the provided test examples CSV file and confirm it appears in your Arize space.
High-intent search queries and answers about installing and using this skill.
Arize dataset versioning is automated by wrapping the official ax CLI to handle test dataset creation, updates, and version control directly within ML evaluation workflows. This prevents manual field mismatches and streamlines model evaluation cycles.
You can append bulk examples to an Arize test dataset by importing them directly from files or inline JSON. This allows ML engineers to quickly add new evaluation examples after model updates without memorizing complex CLI flags.
Yes, full ML evaluation datasets can be exported from Arize for offline analysis and reporting. The skill manages bulk data extraction securely through the ax CLI, preventing truncated exports and ensuring complete dataset retrieval.
The best way to annotate ML evaluation examples in Arize is through bulk annotation workflows that apply labels across multiple records. This automates example annotation directly within your pipeline using the ax CLI.
Yes, the ax CLI is required as it serves as the underlying interface for all dataset interactions. The skill integrates with the ax CLI to perform CRUD operations, schema validation, and secure credential handling for Arize datasets.
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