Below are acceptance criteria for #2 the new version of the ML Model Extension. After all these items are done I think #2 is ready to merge and we can publish a new version 2 release to https://github.com/stac-extensions/ml-model/issues . I'm working on most of these, let me know if you think other acceptance criteria should be included cc @fmigneault

• Incorporate feedback from @fmariv and others at @earthpulse
  • I had a meeting with @fmariv, they're keen to review the extension, provide feedback and see if we can align it more with general ML use cases. They're working on supporting the STAC extension ecosystem for ML as a part of the EOTDL initiative
  • https://hackmd.io/DBRF1sQCS1WmSqygJNKQJQ?view
  • feel free to comment first and then submit a PR against https://github.com/rbavery/dlm-extension/tree/validate
• address Potential improvements from STAC community meeting #5
  • this will involve segregating flat fields from objects that are not meant to be searched on and are instead used for inference model loading, model input and output processing, and documenting in detail the accelerator and runtime details needed to run the model
• improve output section of the spec for popular detection tasks #4 by creating output objects for common detection tasks. At a minimum, start with single label classification, semantic segmentation, object detection. This will help resolve common ambiguities when interpreting model outputs, like the bbox coordinate ordering for object detection. EDIT this can be left for after the v2 release
• resolve how to reference the ML model extension from other STAC items/collections #3, deciding how to refer to model extension metadata. within it's own STAC item/collection json or are these fields composed with common metadata in STAC json representing a spatiotemporal asset?
• New Machine Learning Model Extension Version 2.0.alpha schema and (de)serialization, validation package #2 (comment) create an example and schema for the final spec
• reorganize and update the stac_model extension validation and serialization package according to the final spec: New Machine Learning Model Extension Version 2.0.alpha schema and (de)serialization, validation package #2 (comment)
• fix JSON schema with MLM fields + support pydantic/pystac objects rbavery/dlm-extension#2
• consider inputs from user feedback (Revisiting the ML Model extension stac-extensions/ml-model#13)
• consider community examples applying DLM (https://github.com/sentinel-hub/stac-ml-example)
• handle complex normalization with clipping #8
• handle multiple model artifacts that are associated with a model #9
• Allow various Bands representations of Model Input Object #12
• Link on best practices to STAC check to close discoverability loop #13
• integrate JSON schema representing new properties/fields (mix of https://github.com/crim-ca/dlm-extension/tree/main/json-schema and ML Model schema definition rbavery/dlm-extension#1)
• hyperparameter definition #14
• Proposal: Extend the Classification schema for integration with Machine Learning Model extension stac-extensions/classification#48
• https://github.com/stac-extensions/example-links/issues/4
• Add more example expression objects stac-extensions/processing#31
• add more processing:expression examples for python, docker and generic URI stac-extensions/processing#33
• consideration of end_datetime: null not supported by STAC Core spec
  (Datasets without time radiantearth/stac-spec#1268)