ML-Enabler V3.0 RoadMap

General Directions we would like to explore for the next major release of ml-enabler per conversations with @geohacker + @ingalls

**Developer Support**
- Training data creation — support training data creation for classification models first (and then object detection). The user brings tile endpoint, or S3 bucket list of tiles along with vector file (`geojson`, on S3). ML Enabler facilitates the creation of `tf-records` via AWS batch.
- Visualize training data — visualize training data locations (similar to how we visualize inferences), but color code based on how the training data is divided ie: test/test/val
- Expand classification retraining workflow — expand, further generalize, and mature classification re-training workflows. Start to consider implementing Object Detection re-training workflows
- Segmentation inference workflows - modify download and predict to handle segmentation so segmentation predictions are predicted + displayed as individual geometries
- Post processing hooks — run custom code to do post processing of the inferences. For example, to compare the model inference with existing data in OSM.
- Support uploading/serving models for inference ONNX as a more generalizable entry point for users that want to bring their own model to ml-enabler

**Mapping Tool Integrations**
- MapRoulette — ML Enabler can push predictions to a MapRoulette task for easy mapping. This currently exists, but we want to setup a public instance.
- Tasking Manager — ML Enabler can be immediately integrated into Tasking Manager during the project creation step to identify task complexity.
- iD and JOSM — JOSM has limited support via a plugin. We should scope out what the integration looks like for iD.
- Bring validated model predictions back into ML-Enabler from a mapping campaign like MapRoulette

**Public Demo**
- Use publicly RGB model either from Dev Seed (HV grid classification) or outside of Dev Seed to showcase the whole ML-Enabler workflow into a public Map Roulette
- demo will focused on how iml-enabler can help users
- A key thing to highlight that ML-Enabler can help with is flagging false positives - this is a huge challenge for many existing OSM mapping workflows