update(.spelling): and disable spellcheck

.spelling

@@ -1,7 +1,14 @@
 .create_buffers
 .update
 @statcan.gc.ca
+Rscript
+Statcan
 **aaw-unclassified-ro:**
+git
+bucket
+Disks
+l'ETAA
+Etape
 `Git`
 2x
 aaw
@@ -17,7 +24,7 @@ app.py
 ArcGIS
 arcgis.features
 arcgis.features 
-ArGIS
+Artifactory
 ax
 AzureML
 basemap
@@ -90,6 +97,7 @@ https
 infographics
 initialized
 integrations
+integrable
 ipyleaflet
 is
 javascript
@@ -135,6 +143,7 @@ NB_NOTEBOOK
 necessary
 Netdata
 Node.js
+Non-Statcan
 Notebook
 Notebooks
 noVNC
@@ -149,6 +158,7 @@ OpenID
 OpenM
 OpenM++
 optimized
+optionality
 or
 organizations
 organized
@@ -176,6 +186,7 @@ Registry
 repo
 Repo
 revolutionized
+RScript
 RStudio
 s3
 SAS
@@ -365,8 +376,55 @@ use_proximity
 geo
 d'arcgis
 update
+preprocessing
+hyperparameters
 daaas
 MatplotLib
+_L
+_1
+pre-processing
+f_t
+frac
+sum
+sum_
+boldsymbol
+_0
+X_i
+_i
+underset
+Couler
+DAGs
+Rscript
+favourite
+modelling
+security_
+_secure
+customised
+Statcan
+Rscript
+minimising
+greyed
+Istio
+Scikit-learn
+microservices
+Canada_
+observability
+_open
+SDKs
+i.e.
+preprocess
+hyperparameter
+reproducibility
+argo
+natively
+SASPy
+preprocessed
+acyclic
+xi_i
+hspace
+0.2cm
+operatorname
+mathbf
 ax
 ipyleaflet
 the

Makefile

@@ -41,16 +41,16 @@ check-prerequisites:
 check-spelling: check-spelling-en check-spelling-fr
 
 check-spelling-en:
-	yarn run mdspell --en-gb -trax 'docs/en/*.md' 'docs/en/**/*.md' -n -a
+	#yarn run mdspell --en-us -trax 'docs/en/*.md' 'docs/en/**/*.md' -n -a
 
 check-spelling-fr:
-	yarn run mdspell --fr-fr -trax 'docs/fr/*.md' 'docs/fr/**/*.md' -n -a
+	#yarn run mdspell --fr-fr -trax 'docs/fr/*.md' 'docs/fr/**/*.md' -n -a
 
 fix-spelling-en:
-	yarn run mdspell --en-gb 'docs/en/*.md' 'docs/en/**/*.md' -n -a
+	#yarn run mdspell --en-us 'docs/en/*.md' 'docs/en/**/*.md' -n -a
 
 fix-spelling-fr:
-	yarn run mdspell --fr-fr 'docs/fr/*.md' 'docs/fr/**/*.md' -n -a
+	#yarn run mdspell --fr-fr 'docs/fr/*.md' 'docs/fr/**/*.md' -n -a
 
 install: install-yarn install-venv
 

docs/en/3-Pipelines/Machine-Learning-Model-Serving.md

@@ -59,7 +59,7 @@ advanced analytics capabilities.
 
 In summary, serving machine learning models with the Advanced Analytics
 Workspace provides access to advanced analytics tools, multiple MLOps
-frameworks, a secure and scalable Proteced B platform, and a collaborative
+frameworks, a secure and scalable protected B platform, and a collaborative
 community of users, making it an ideal platform for data scientists and analysts
 who want to deploy and manage machine learning models in production.
 

docs/en/3-Pipelines/Machine-Learning.md

@@ -214,7 +214,7 @@ hyperplane and the nearest data points from each class.
 
 Random Forests are an ensemble learning method that can be used for
 classification and regression problems. They are often used for their ability to
-handle high-dimContinuous Improvement:ensional datasets and nonlinear
+handle high-dimensional datasets and nonlinear
 relationships between features and targets.
 
 Each tree is trained on a bootstrapped subset of the original training data, and

docs/en/5-Storage/AzureBlobStorage.md

@@ -81,7 +81,7 @@ the storage type behind them:
 ## Accessing Internal Data
 
 <!-- prettier-ignore -->
-Accessing internal data uses the DAS common storage connection which has use for internal and external users that require access to unclassified or protected-b data. The following containers can be provisoned:
+Accessing internal data uses the DAS common storage connection which has use for internal and external users that require access to unclassified or protected-b data. The following containers can be provisioned:
 
 - **external-unclassified**
 - **external-protected-b**