Context

Before building data pipelines or integrations, we need a concrete and shared understanding of what the unified GPU dashboard should look like and which questions it must answer.

A clear mock is required to align all stakeholders on:

what data is needed, how it is presented, and how it will actually be used for decision-making.

This story intentionally comes first, as it drives the data model, architecture, and integration requirements

 

Objective

Create a dashboard mock that defines the target end-state UX and explicitly documents all required data fields, filters, and views.

 

The mock will serve as the contract for:

data collection, normalization, and architecture decisions in subsequent stories.

 

 

Scope

In scope:

• Create a visual mock (low or mid fidelity is sufficient)

• Define all dashboard views and sections

• Define filters and drill-down dimensions

• Explicitly list all required data fields per view

• Define freshness expectations per data type (real-time vs near real-time)

Out of scope:

• Backend implementation

• Data ingestion pipelines

• Production dashboard setup

Dashboard Capabilities to Cover

The mock must clearly show how a user can:

• See total GPU inventory

• Filter by team, environment, cloud, and cluster

• Distinguish idle vs used GPUs

• See usage over time (patterns, not just current state)

• Associate GPUs or GPU pools with estimated cost

• Identify underutilization and inefficiencies

• Answer “who used which GPUs and when”

 

Deliverables

• Dashboard mock (image or doc, etc)

• Explicit list of required data fields per widget/view

• Defined filters and dimensions

• Notes on assumptions and open questions

 

DoD

This story is complete when:

• A mock exists and is reviewable

• Required data fields are clearly documented

• Stakeholders agree the mock answers real managerial questions

• The mock can be used to drive architecture and data requirements