Feature Overview (mandatory - Complete while in New status)

• When users generate a lot of samples, they will have the option to run subset selection method to get a minimal set of samples representative of original dataset. 
• Subset Selection algorithm as developed by research computes embeddings of the samples and then tries to iteratively find a minimal subset which maximizes the coverage of the dataset.

Goals (mandatory - Complete while in New status)
Provide high-level goal statement, providing user context and expected user outcome(s) for this Feature

• End user with a large dataset, which might require higher compute, training time, other resources can reduce the size of the input dataset 

Requirements (mandatory -_ Complete while in Refinement status):

• Given an input dataset, subset selection outputs a smaller set representative of the original dataset. 

Done - Acceptance Criteria (mandatory - Complete while in Refinement status):

• Output of subset selection is representative of original dataset
• Smoke test to verify few use cases with subset selection (model is being trained efficiently)

Use Cases - i.e. User Experience & Workflow: (Initial completion while in Refinement status):

Out of Scope __(Initial completion while in Refinement status):

N/A

Documentation Considerations __(Initial completion while in Refinement status):
Provide information that needs to be considered and planned so that documentation will meet customer needs. If the feature extends existing functionality, provide a link to its current documentation..
<your text here>

Questions to Answer __(Initial completion while in Refinement status):

In the SDG flow, is subset selection done during preprocessing or post processing or can be done at both places ?

Background and Strategic Fit (Initial completion while in Refinement status):
Provide any additional context is needed to frame the feature.
<your text here>

Customer Considerations __(Initial completion while in Refinement status):
Provide any additional customer-specific considerations that must be made when designing and delivering the Feature.
<your text here>