Fix more links

Author: aversey

README.md

@@ -1,6 +1,6 @@
-# Documentation landing page
+# Hopsworks Documentation
 
-This is the source of the landing page for <https://docs.hopsworks.ai>
+This is the source of the Hopsworks Documentation published at <https://docs.hopsworks.ai>.
 
 ## Build instructions
 
@@ -57,3 +57,85 @@ linkchecker http://127.0.0.1:8000/
 # If ok just kill the server
 kill -9 $SERVER_PID
 ```
+
+## Setup and Build Documentation
+
+We use `mkdocs` together with `mike` ([for versioning](https://github.com/jimporter/mike/)) to build the documentation and a plugin called `keras-autodoc` to auto generate Python API documentation from docstrings.
+
+**Background about `mike`:**
+`mike` builds the documentation and commits it as a new directory to the gh-pages branch.
+Each directory corresponds to one version of the documentation.
+Additionally, `mike` maintains a json in the root of gh-pages with the mappings of versions/aliases for each of the directories available.
+With aliases you can define extra names like `dev` or `latest`, to indicate stable and unstable releases.
+
+### Versioning on docs.hopsworks.ai
+
+On docs.hopsworks.ai we implement the following versioning scheme:
+
+- current master branches (e.g. of hopsworks corresponding to master of Hopsworks): rendered as current Hopsworks snapshot version, e.g. **4.0.0-SNAPSHOT [dev]**, where `dev` is an alias to indicate that this is an unstable version.
+- the latest release: rendered with full current version, e.g. **3.8.0 [latest]** with `latest` alias to indicate that this is the latest stable release.
+- previous stable releases: rendered without alias, e.g. **3.4.4**.
+
+### 4. Build Instructions
+
+For this you can either checkout and make a local copy of the `upstream/gh-pages` branch, where `mike` maintains the current state of docs.hopsworks.ai, or just build documentation for the branch you are updating:
+
+Building *one* branch:
+
+Checkout your dev branch with modified docs:
+
+```bash
+git checkout [dev-branch]
+```
+
+Generate API docs if necessary:
+
+```bash
+python auto_doc.py
+```
+
+Build docs with a version and alias
+
+```bash
+mike deploy [version] [alias] --update-alias
+
+# for example, if you are updating documentation to be merged to master,
+# which will become the new SNAPSHOT version:
+mike deploy 4.0.0-SNAPSHOT dev --update-alias
+
+# if you are updating docs of the latest stable release branch
+mike deploy [version] latest --update-alias
+
+# if you are updating docs of a previous stable release branch
+mike deploy [version]
+```
+
+If no gh-pages branch existed in your local repository, this will have created it.
+
+**Important**: If no previous docs were built, you will have to choose a version as default to be loaded as index, as follows
+
+```bash
+mike set-default [version-or-alias]
+```
+
+You can now checkout the gh-pages branch and serve:
+
+```bash
+git checkout gh-pages
+mike serve
+```
+
+You can also list all available versions/aliases:
+
+```bash
+mike list
+```
+
+Delete and reset your local gh-pages branch:
+
+```bash
+mike delete --all
+
+# or delete single version
+mike delete [version-or-alias]
+```

docs/setup_installation/admin/oauth2/create-okta-client.md

@@ -47,7 +47,7 @@ You can also create mappings from groups in Okta to groups in Hopsworks.
 To achieve this you need to configure Okta to send _Groups_ with user information.
 To do this go to _Applications_ and select your application name.
 In the _Sign On_ tab click edit _OpenID Connect ID Token_ and select **Filter** for _Groups claim type_, then for _Groups claim filter_ add **groups** as the claim name, select **Match Regex** from the dropdown and .* (dot star) as Regex to match all groups.
-See [Group mapping](./create-client.md#group-mapping) on how to do the mapping in Hopsworks.
+See [Group mapping](./create-client.md#step-2-add-group-mappings) on how to do the mapping in Hopsworks.
 
   <figure>
     <img src="../../../../assets/images/admin/oauth2/okta-groups.png" alt="Group claim" />

docs/user_guides/fs/data_source/creation/redshift.md

@@ -69,7 +69,7 @@ Start by giving it a **name** and an optional **description**.
 
 !!! warning "Session Duration"
     By default, the session duration that the role will be assumed for is 1 hour or 3600 seconds.
-    This means if you want to use the data source for example to [read or create an external Feature Group from Redshift](../usage.md##creating-an-external-feature-group), the operation cannot take longer than one hour.
+    This means if you want to use the data source for example to [read or create an external Feature Group from Redshift](../usage.md#creating-an-external-feature-group), the operation cannot take longer than one hour.
 
     Your administrator can change the default session duration for AWS data sources, by first [increasing the max session duration of the IAM Role](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_use.html#id_roles_use_view-role-max-session) that you are assuming.
     And then changing the `fs_data_source_session_duration` [configuration property](../../../../setup_installation/admin/variables.md) to the appropriate value in seconds.

docs/user_guides/fs/feature_group/on_demand_transformations.md

@@ -10,7 +10,7 @@ Each on-demand transformation function can generate one or multiple on-demand fe
 If the on-demand transformation function returns a single feature, it is automatically assigned the same name as the transformation function.
 However, if it returns multiple features, they are by default named using the format `functionName_outputColumnNumber`.
 For instance, in the example below, the on-demand transformation function `transaction_age` produces an on-demand feature named `transaction_age` and the on-demand transformation function `stripped_strings` produces the on-demand features names `stripped_strings_0` and `stripped_strings_1`.
-Alternatively, the name of the resulting on-demand feature can be explicitly defined using the [`alias`](../transformation_functions.md#specifying-output-features–names-for-transformation-functions) function.
+Alternatively, the name of the resulting on-demand feature can be explicitly defined using the [`alias`](../transformation_functions.md#specifying-output-features-names-for-transformation-functions) function.
 
 !!! warning "On-demand transformation"
     All on-demand transformation functions attached to a feature group must have unique names and, in contrast to model-dependent transformations, they do not have access to training dataset statistics.

docs/user_guides/fs/feature_view/batch-data.md

@@ -64,7 +64,7 @@ Please note that transformed batch data can only be returned in the python clien
 feature_view.init_batch_scoring(training_dataset_version=1)
 ```
 
-It is important to note that in addition to the filters defined in feature view, [extra filters](./training-data.md#Extra-filters) will be applied if they are defined in the given training dataset version.
+It is important to note that in addition to the filters defined in feature view, [extra filters](./training-data.md#extra-filters) will be applied if they are defined in the given training dataset version.
 
 ## Retrieving untransformed batch data
 

docs/user_guides/fs/feature_view/model-dependent-transformations.md

@@ -23,7 +23,7 @@ Hopsworks by default generates default names of transformed features output by a
 The generated names follows a naming convention structured as `functionName_features_outputColumnNumber` if the transformation function outputs multiple columns and `functionName_features` if the transformation function outputs one column.
 For instance, for the function named `add_one_multiple` that outputs multiple columns in the example given below, produces output columns that would be labeled as  `add_one_multiple_feature1_feature2_feature3_0`,  `add_one_multiple_feature1_feature2_feature3_1`  and   `add_one_multiple_feature1_feature2_feature3_2`.
 The function named `add_two` that outputs a single column in the example given below, produces a single output column names as `add_two_feature`.
-Additionally, Hopsworks also allows users to specify custom names for transformed feature using the [`alias`](../transformation_functions.md#specifying-output-features–names-for-transformation-functions) function.
+Additionally, Hopsworks also allows users to specify custom names for transformed feature using the [`alias`](../transformation_functions.md#specifying-output-features-names-for-transformation-functions) function.
 
 === "Python"
 

docs/user_guides/mlops/serving/deployment.md

@@ -219,7 +219,7 @@ Artifact versions are unique for the same model version.
 
 When a new deployment is created, a new artifact version is generated in two cases:
 
-- the artifact version in the predictor is set to `CREATE` (see [Artifact Version](./predictor.md#artifact_version))
+- the artifact version in the predictor is set to `CREATE` (see [Artifact Version](./predictor.md#environment-variables))
 - no model artifact with the same files has been created before.
 
 Inside a model deployment, the local path to the artifact files is stored in the `ARTIFACT_FILES_PATH` environment variable (see [environment variables](../serving/predictor.md#environment-variables)).