Here's a summary of the changes from the last pull request:

- [🎉 NEW] We now support remote cluster creation on any Azure Cloud
  Environment, abstracting the requirements of different cloud
  environments and backend services. This means we now support
  deployments in sovereign clouds, such as the [US Government
  Cloud](https://learn.microsoft.com/en-us/azure/azure-government/documentation-government-welcome),
  the [German Cloud](https://learn.microsoft.com/en-us/azure/germany/),
  and the [China Cloud](https://learn.microsoft.com/en-us/azure/china/overview-operations),
  in addition to the Azure Public Cloud.
- [📈 IMPROVEMENT] We upgraded terraform provider to version 3.89.0 to
  support cluster setup through Azure Cloud Shell.
- [⚒️ FIX] We fixed an issue that would cause the local cluster setup to
  fail in case the cluster name had underscores. Related to issue #133.
- [📈 IMPROVEMENT] We revised the notebook dependencies and changed the
  default environment and package manager from Conda to
  [Micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html).
  Environments should build considerably faster and present fewer
  dependency issues. Related to issues #141 and #144.
- [🎉 NEW] We added instructions on setting up the remote cluster using
  Azure Cloud Shell to the [Remote Cluster User
  Guide](https://microsoft.github.io/farmvibes-ai/docfiles/markdown/AKS.html).
- [⚒️ FIX] We fixed a bug that prevented the REST API documentation page
  to be rendered correctly. Related to issue #131.

----

Co-authored-by: Bruno Silva <brunosilva@microsoft.com>
Co-authored-by: Peter Hammond <v-phammond@microsoft.com>
Co-authored-by: Rafael Padilha <rpadilha@microsoft.com>
Co-authored-by: Renato Luiz de Freitas Cunha <renato.cunha@microsoft.com>
Co-authored-by: Rick Gutierrez <ricardgu@microsoft.com>

docs/source/docfiles/markdown/WORKFLOW_LIST.md

@@ -124,7 +124,6 @@ Below is a list of all available workflows within the FarmVibes.AI platform. For
 - [`heatmap/classification` 📄](workflow_yaml/data_processing/heatmap/classification.md): Utilizes input Sentinel-2 satellite imagery & the sensor samples as labeled data that contain nutrient information (Nitrogen, Carbon, pH, Phosphorus) to train a model using Random Forest classifier. The inference operation predicts nutrients in soil for the chosen farm boundary.
 
 
-
 - [`index/index` 📄](workflow_yaml/data_processing/index/index.md): Computes an index from the bands of an input raster.
 
 - [`linear_trend/chunked_linear_trend` 📄](workflow_yaml/data_processing/linear_trend/chunked_linear_trend.md): Computes the pixel-wise linear trend of a list of rasters (e.g. NDVI).