Add broken links checker

.github/workflows/check-markdown-links.yml

@@ -0,0 +1,20 @@
+---
+name: Check Markdown Links
+on:
+  pull_request:
+    types: [opened, synchronize, reopened]
+jobs:
+  check-links:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.x
+      - name: Install dependencies
+        run: pip install PyGithub
+      - name: Run markdown link checker
+        run: ./scripts/check_and_comment.sh docs
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

docs/book/component-guide/artifact-stores/custom.md

@@ -156,7 +156,7 @@ zenml artifact-store flavor register flavors.my_flavor.MyArtifactStoreFlavor
 ```
 
 {% hint style="warning" %}
-ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/setting-up-a-project-repository/best-practices.md) of initializing zenml at the root of your repository.
+ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/infrastructure-deployment/infrastructure-as-code/best-practices.md) of initializing zenml at the root of your repository.
 
 If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually, it's better to not have to rely on this mechanism and initialize zenml at the root.
 {% endhint %}

docs/book/component-guide/container-registries/custom.md

@@ -98,7 +98,7 @@ zenml container-registry flavor register flavors.my_flavor.MyContainerRegistryFl
 ```
 
 {% hint style="warning" %}
-ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/setting-up-a-project-repository/best-practices.md) of initializing zenml at the root of your repository.
+ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/infrastructure-deployment/infrastructure-as-code/best-practices.md) of initializing zenml at the root of your repository.
 
 If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually it's better to not have to rely on this mechanism, and initialize zenml at the root.
 {% endhint %}

docs/book/component-guide/data-validators/custom.md

@@ -40,7 +40,7 @@ zenml data-validator flavor register flavors.my_flavor.MyDataValidatorFlavor
 ```
 
 {% hint style="warning" %}
-ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/setting-up-a-project-repository/best-practices.md) of initializing zenml at the root of your repository.
+ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/infrastructure-deployment/infrastructure-as-code/best-practices.md) of initializing zenml at the root of your repository.
 
 If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually it's better to not have to rely on this mechanism, and initialize zenml at the root.
 {% endhint %}

docs/book/component-guide/experiment-trackers/custom.md

@@ -37,7 +37,7 @@ zenml experiment-tracker flavor register flavors.my_flavor.MyExperimentTrackerFl
 ```
 
 {% hint style="warning" %}
-ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/setting-up-a-project-repository/best-practices.md) of initializing zenml at the root of your repository.
+ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/infrastructure-deployment/infrastructure-as-code/best-practices.md) of initializing zenml at the root of your repository.
 
 If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually, it's better to not have to rely on this mechanism and initialize zenml at the root.
 {% endhint %}

docs/book/component-guide/experiment-trackers/experiment-trackers.md

@@ -13,7 +13,7 @@ through Experiment Tracker stack components. This establishes a clear link betwe
 Related concepts:
 
 * the Experiment Tracker is an optional type of Stack Component that needs to be registered as part of your
-  ZenML [Stack](/docs/book/user-guide/production-guide/understand-stacks.md).
+  ZenML [Stack](../../user-guide/production-guide/understand-stacks.md).
 * ZenML already provides versioning and tracking for the pipeline artifacts by storing artifacts in
   the [Artifact Store](../artifact-stores/artifact-stores.md).
 

docs/book/component-guide/image-builders/custom.md

@@ -88,7 +88,7 @@ zenml image-builder flavor register flavors.my_flavor.MyImageBuilderFlavor
 ```
 
 {% hint style="warning" %}
-ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/setting-up-a-project-repository/best-practices.md) of initializing zenml at the root of your repository.
+ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/infrastructure-deployment/infrastructure-as-code/best-practices.md) of initializing zenml at the root of your repository.
 
 If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually it's better to not have to rely on this mechanism, and initialize zenml at the root.
 {% endhint %}

docs/book/component-guide/image-builders/image-builders.md

@@ -38,7 +38,7 @@ zenml image-builder flavor list
 ### How to use it
 
 You don't need to directly interact with any image builder in your code. As long as the image builder that you want to
-use is part of your active [ZenML stack](/docs/book/user-guide/production-guide/understand-stacks.md), it will be used
+use is part of your active [ZenML stack](../../user-guide/production-guide/understand-stacks.md), it will be used
 automatically by any component that needs to build container images.
 
 <!-- For scarf -->

docs/book/component-guide/model-deployers/custom.md

@@ -143,7 +143,7 @@ zenml model-deployer flavor register flavors.my_flavor.MyModelDeployerFlavor
 ```
 
 {% hint style="warning" %}
-ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/setting-up-a-project-repository/best-practices.md) of initializing zenml at the root of your repository.
+ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/infrastructure-deployment/infrastructure-as-code/best-practices.md) of initializing zenml at the root of your repository.
 
 If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually, it's better to not have to rely on this mechanism and initialize zenml at the root.
 {% endhint %}

docs/book/component-guide/orchestrators/airflow.md

@@ -159,7 +159,7 @@ of your Airflow deployment.
 {% hint style="info" %}
 ZenML will build a Docker image called `<CONTAINER_REGISTRY_URI>/zenml:<PIPELINE_NAME>` which includes your code and use
 it to run your pipeline steps in Airflow. Check
-out [this page](/docs/book/how-to/customize-docker-builds/README.md) if you want to learn
+out [this page](../../how-to/customize-docker-builds/README.md) if you want to learn
 more about how ZenML builds these images and how you can customize them.
 {% endhint %}
 
@@ -204,13 +204,13 @@ The username will always be `admin`.
 For additional configuration of the Airflow orchestrator, you can pass `AirflowOrchestratorSettings` when defining or
 running your pipeline. Check out
 the [SDK docs](https://sdkdocs.zenml.io/latest/integration\_code\_docs/integrations-airflow/#zenml.integrations.airflow.flavors.airflow\_orchestrator\_flavor.AirflowOrchestratorSettings)
-for a full list of available attributes and [this docs page](/docs/book/how-to/pipeline-development/use-configuration-files/README.md) for
+for a full list of available attributes and [this docs page](../../how-to/pipeline-development/use-configuration-files/README.md) for
 more information on how to specify settings.
 
 #### Enabling CUDA for GPU-backed hardware
 
 Note that if you wish to use this orchestrator to run steps on a GPU, you will need to
-follow [the instructions on this page](/docs/book/how-to/pipeline-development/training-with-gpus/README.md) to ensure that it
+follow [the instructions on this page](../../how-to/pipeline-development/training-with-gpus/README.md) to ensure that it
 works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full
 acceleration.
 
@@ -297,7 +297,7 @@ the [original module](https://github.com/zenml-io/zenml/blob/main/src/zenml/inte
 . For this reason, we suggest starting by copying the original and modifying it according to your needs.
 
 Check out our docs on how to apply settings to your
-pipelines [here](/docs/book/how-to/pipeline-development/use-configuration-files/README.md).
+pipelines [here](../../how-to/pipeline-development/use-configuration-files/README.md).
 
 For more information and a full list of configurable attributes of the Airflow orchestrator, check out the [SDK Docs](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-airflow/#zenml.integrations.airflow.orchestrators.airflow_orchestrator.AirflowOrchestrator) .
 

docs/book/component-guide/orchestrators/azureml.md

@@ -42,7 +42,7 @@ In order to use an AzureML orchestrator, you need to first
 deploy [ZenML to the cloud](../../getting-started/deploying-zenml/README.md). 
 It would be recommended to deploy ZenML in the same region as you plan on 
 using for AzureML, but it is not necessary to do so. You must ensure that 
-you are [connected to the remote ZenML server](../../how-to/connecting-to-zenml/connect-in-with-your-user-interactive.md)
+you are [connected to the remote ZenML server](../../how-to/manage-zenml-server/connecting-to-zenml/connect-in-with-your-user-interactive.md)
 before using this stack component.
 
 ## How to use it

docs/book/component-guide/orchestrators/custom.md

@@ -100,7 +100,7 @@ zenml orchestrator flavor register flavors.my_flavor.MyOrchestratorFlavor
 ```
 
 {% hint style="warning" %}
-ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/setting-up-a-project-repository/best-practices.md) of initializing zenml at the root of your repository.
+ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/infrastructure-deployment/infrastructure-as-code/best-practices.md) of initializing zenml at the root of your repository.
 
 If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually, it's better to not have to rely on this mechanism and initialize zenml at the root.
 {% endhint %}

docs/book/component-guide/orchestrators/orchestrators.md

@@ -52,9 +52,9 @@ zenml orchestrator flavor list
 ### How to use it
 
 You don't need to directly interact with any ZenML orchestrator in your code. As long as the orchestrator that you want
-to use is part of your active [ZenML stack](/docs/book/user-guide/production-guide/understand-stacks.md), using the
+to use is part of your active [ZenML stack](../../user-guide/production-guide/understand-stacks.md), using the
 orchestrator is as simple as executing a Python file
-that [runs a ZenML pipeline](/docs/book/user-guide/starter-guide/starter-guide.md):
+that [runs a ZenML pipeline](../../user-guide/starter-guide/starter-project.md):
 
 ```shell
 python file_that_runs_a_zenml_pipeline.py

docs/book/component-guide/step-operators/custom.md

@@ -97,7 +97,7 @@ zenml step-operator flavor register flavors.my_flavor.MyStepOperatorFlavor
 ```
 
 {% hint style="warning" %}
-ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/setting-up-a-project-repository/best-practices.md) of initializing zenml at the root of your repository.
+ZenML resolves the flavor class by taking the path where you initialized zenml (via `zenml init`) as the starting point of resolution. Therefore, please ensure you follow [the best practice](../../how-to/infrastructure-deployment/infrastructure-as-code/best-practices.md) of initializing zenml at the root of your repository.
 
 If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually, it's better to not have to rely on this mechanism and initialize zenml at the root.
 {% endhint %}

docs/book/component-guide/step-operators/modal.md

@@ -63,7 +63,7 @@ ZenML will build a Docker image which includes your code and use it to run your
 #### Additional configuration
 
 You can specify the hardware requirements for each step using the
-`ResourceSettings` class as described in our documentation on [resource settings](../../how-to/training-with-gpus/training-with-gpus.md):
+`ResourceSettings` class as described in our documentation on [resource settings](../../how-to/pipeline-development/training-with-gpus/README.md):
 
 ```python
 from zenml.config import ResourceSettings

docs/book/component-guide/step-operators/step-operators.md

@@ -48,7 +48,7 @@ zenml step-operator flavor list
 ### How to use it
 
 You don't need to directly interact with any ZenML step operator in your code. As long as the step operator that you
-want to use is part of your active [ZenML stack](/docs/book/user-guide/production-guide/understand-stacks.md), you can simply
+want to use is part of your active [ZenML stack](../../user-guide/production-guide/understand-stacks.md), you can simply
 specify it in the `@step` decorator of your step.
 
 ```python

docs/book/getting-started/deploying-zenml/custom-secret-stores.md

@@ -97,6 +97,6 @@ If you want to create your own custom secrets store implementation, you can foll
 
 1. Create a class that inherits from the `zenml.zen_stores.secrets_stores.base_secrets_store.BaseSecretsStore` base class and implements the `abstractmethod`s shown in the interface above. Use `SecretsStoreType.CUSTOM` as the `TYPE` value for your secrets store class.
 2. If you need to provide any configuration, create a class that inherits from the `SecretsStoreConfiguration` class and add your configuration parameters there. Use that as the `CONFIG_TYPE` value for your secrets store class.
-3. To configure the ZenML server to use your custom secrets store, make sure your code is available in the container image that is used to run the ZenML server. Then, use environment variables or helm chart values to configure the ZenML server to use your custom secrets store, as covered in the [deployment guide](../README.md).
+3. To configure the ZenML server to use your custom secrets store, make sure your code is available in the container image that is used to run the ZenML server. Then, use environment variables or helm chart values to configure the ZenML server to use your custom secrets store, as covered in the [deployment guide](./README.md).
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>

docs/book/getting-started/deploying-zenml/deploy-with-custom-image.md

@@ -6,7 +6,7 @@ description: Deploying ZenML with custom Docker images.
 
 In most cases, deploying ZenML with the default `zenmlhub/zenml-server` Docker image should work just fine. However, there are some scenarios when you might need to deploy ZenML with a custom Docker image:
 
-* You have implemented a custom artifact store for which you want to enable [artifact visualizations](../../how-to/handle-data-artifacts/visualize-artifacts.md) or [step logs](../../how-to/setting-up-a-project-repository/best-practices.md#logging) in your dashboard.
+* You have implemented a custom artifact store for which you want to enable [artifact visualizations](../../how-to/data-artifact-management/visualize-artifacts/README.md) or [step logs](../../../how-to/setting-up-a-project-repository/best-practices.md#logging) in your dashboard.
 * You have forked the ZenML repository and want to deploy a ZenML server based on your own fork because you made changes to the server / database logic.
 
 {% hint style="warning" %}

docs/book/getting-started/deploying-zenml/deploy-with-docker.md

@@ -199,7 +199,7 @@ These configuration options are only relevant if you're using Hashicorp Vault as
 {% endtab %}
 
 {% tab title="Custom" %}
-These configuration options are only relevant if you're using a custom secrets store backend implementation. For this to work, you must have [a custom implementation of the secrets store API](manage-the-deployed-services/custom-secret-stores.md) in the form of a class derived from `zenml.zen_stores.secrets_stores.base_secrets_store.BaseSecretsStore`. This class must be importable from within the ZenML server container, which means you most likely need to mount the directory containing the class into the container or build a custom container image that contains the class.
+These configuration options are only relevant if you're using a custom secrets store backend implementation. For this to work, you must have [a custom implementation of the secrets store API](custom-secret-stores.md) in the form of a class derived from `zenml.zen_stores.secrets_stores.base_secrets_store.BaseSecretsStore`. This class must be importable from within the ZenML server container, which means you most likely need to mount the directory containing the class into the container or build a custom container image that contains the class.
 
 The following configuration option is required: