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ACTIVE WORKSPACEHome

Purpose, outputs, architecture, and a clear route into the API design workspace.

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Design AI-ready APIs with clear, testable contracts. dataset contracts|

A structured browser workspace for designing dataset services, inference endpoints, experiment registries, model metrics, and health checks without hiding the underlying request and response contracts.

Original template
user@metricforge: ~/chanfana-openapi-ai-data-studio
8AI endpoints4data domains3.1OpenAPI targetLocalmock runner
01 | CLEAR PRODUCT DEFINITION

Useful by design, honest about scope.

Design and explain HTTP interfaces for datasets, inference, experiments, model registries, and health checks in a contract-first workflow.

Primary use

API design classes, capstone demonstrations, schema reviews, and model-serving architecture planning.

A structured browser workspace for designing dataset services, inference endpoints, experiment registries, model metrics, and health checks without hiding the underlying request and response contracts.

GitHub repository ↗
Produces

Concrete, reusable outputs

OpenAPI JSON, endpoint maps, mock responses, dataset profiles, and model evaluation payloads.

Boundary

No exaggerated capability claims

The static edition validates and demonstrates interface design; a real model, database, authentication layer, and server-side validation still require a backend.

02 | WORKFLOW

One focused path from input to output.

Every stage is separated so the interface stays readable, reviewable, and useful for AI and Data Science learning.

01

Define

Choose the API domain, operation, path, and expected data contract.

02

Validate

Check request bodies, response shapes, and failure cases.

03

Simulate

Run deterministic mock requests before connecting production services.

04

Export

Download a portable contract for implementation and review.