Use https

Author: aversey

docs/setup_installation/admin/ldap/configure-server.md

@@ -63,7 +63,7 @@ asadmin create-jndi-resource \
 
 ### Step 2: Server Configuration for Kerberos
 
-The Kerberos attributes are used to configure [SPNEGO](http://spnego.sourceforge.net/).
+The Kerberos attributes are used to configure [SPNEGO](https://spnego.sourceforge.net/).
 SPNEGO is used to establish a secure context between the requester and the application server when using Kerberos
 authentication.
 

docs/user_guides/fs/transformation_functions.md

@@ -8,7 +8,7 @@ These can be used along with [feature groups](./feature_group/index.md) and [fe
 
 ## Custom Transformation Function Creation
 
-User-defined transformation functions can be created in Hopsworks using the [`@udf`](http://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/udf/) decorator.
+User-defined transformation functions can be created in Hopsworks using the [`@udf`](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/udf/) decorator.
 These functions can be either implemented as pure Python UDFs or Pandas UDFs (User-Defined Functions).
 
 Hopsworks offers three execution modes to control the execution of transformation functions during training dataset creation, batch inference, and online inference.
@@ -34,7 +34,7 @@ These functions can be part of a library [installed](../../user_guides/projects/
     Definition transformation function within a Jupyter notebook is only supported in Python Kernel.
     In a PySpark Kernel transformation function have to defined as modules or added when starting a Jupyter notebook.
 
-The `@udf` decorator in Hopsworks creates a metadata class called [`HopsworksUdf`](http://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/hopsworks_udf/).
+The `@udf` decorator in Hopsworks creates a metadata class called [`HopsworksUdf`](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/hopsworks_udf/).
 This class manages the necessary operations to execute the transformation function.
 
 The decorator accepts three parameters:
@@ -210,7 +210,7 @@ In the example below, the columns mapped to the arguments `feature1` and `fea
 
 ### Specifying output features names for transformation functions
 
-The [`alias`](http://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/transformation_functions_api/#alias) function of a transformation function allows the specification of names of transformed features generated by the transformation function.
+The [`alias`](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/transformation_functions_api/#alias) function of a transformation function allows the specification of names of transformed features generated by the transformation function.
 Each name must be uniques and should be at-most 63 characters long.
 If no name is provided via the `alias` function, Hopsworks generates default output feature names when [on-demand](./feature_group/on_demand_transformations.md) or [model-dependent](./feature_view/model-dependent-transformations.md) transformation functions are created.
 
@@ -231,7 +231,7 @@ If no name is provided via the `alias` function, Hopsworks generates default out
 ### Training dataset statistics
 
 A keyword argument `statistics` can be defined in the transformation function if it requires training dataset statistics for any of its arguments.
-The `statistics` argument must be assigned an instance of the class [`TransformationStatistics`](http://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/transformation_statistics/) as the default value.
+The `statistics` argument must be assigned an instance of the class [`TransformationStatistics`](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/transformation_statistics/) as the default value.
 The `TransformationStatistics` instance must be initialized using the names of the arguments requiring statistics.
 
 !!! warning "Transformation Statistics"
@@ -240,7 +240,7 @@ The `TransformationStatistics` instance must be initialized using the names of
     Training dataset statistics are not available for on-demand transformations.
 
 The `TransformationStatistics` instance contains separate objects with the same name as the arguments used to initialize it.
-These objects encapsulate statistics related to the argument as instances of the class [`FeatureTransformationStatistics`](http://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_transformation_statistics/).
+These objects encapsulate statistics related to the argument as instances of the class [`FeatureTransformationStatistics`](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_transformation_statistics/).
 Upon instantiation, instances of `FeatureTransformationStatistics` contain `None` values and are updated with the required statistics after the creation of a training dataset.
 
 === "Python"
@@ -274,7 +274,7 @@ By including the context argument, you can pass the necessary data as a dictiona
 
 ## Saving to the Feature Store
 
-To save a transformation function to the feature store, use the function `create_transformation_function`. It creates a [`TransformationFunction`](http://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/transformation_functions_api/) object which can then be saved by calling the save function.
+To save a transformation function to the feature store, use the function `create_transformation_function`. It creates a [`TransformationFunction`](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/transformation_functions_api/) object which can then be saved by calling the save function.
 The save function will throw an error if another transformation function with the same name and version is already saved in the feature store.
 
 === "Python"