Fix more links (#3436)

* Enhance link validity checks in link_checker.py

- Added an additional check for Gitbook URLs to identify pages that return a 200 status code but contain a "noindex" meta tag, indicating a non-existent page.
- Updated the transform_relative_link function to skip links to assets and Gitbook resources.
- Modified the replace_links_in_file function to also skip links to assets and Gitbook resources, ensuring cleaner link processing.

* Fix internal links inside component-guide

* Fix internal links inside getting-started section

* Refactor link replacement logic in link_checker.py

- Introduced a mechanism to track valid replacements, ensuring only valid links are applied during the replacement process.
- Enhanced the handling of inline and reference-style links by collecting potential replacements before applying them.
- Updated the logic to apply replacements conditionally based on validation results, improving the accuracy of link processing.
- Removed unnecessary dry run checks and streamlined the writing of modified content back to the file.

* Fix internal links in how-to section

* Fixing edge case links inside how-to section

---------

Co-authored-by: Hamza Tahir <[email protected]>

Author: strickvl

docs/book/component-guide/alerters/discord.md

@@ -35,7 +35,7 @@ zenml integration install discord -y
 ```
 
 {% hint style="info" %}
-See the [Integrations](../component-guide.md) page for more details on ZenML integrations and how to install and\
+See the [Integrations](https://docs.zenml.io/component-guide) page for more details on ZenML integrations and how to install and\
 use them.
 {% endhint %}
 

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

@@ -15,7 +15,7 @@ Not all objects returned by pipeline steps are physically stored in the Artifact
 If you need to store _a particular type of pipeline artifact_ in a different medium (e.g. use an external model registry to store model artifacts, or an external data lake or data warehouse to store dataset artifacts), you can write your own [Materializer](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/handle-custom-data-types) to implement the custom logic required for it. In contrast, if you need to use an entirely different storage backend to store artifacts, one that isn't already covered by one of the ZenML integrations, you can [extend the Artifact Store abstraction](custom.md) to provide your own Artifact Store implementation.
 {% endhint %}
 
-In addition to pipeline artifacts, the Artifact Store may also be used as storage backed by other specialized stack components that need to store their data in the form of persistent object storage. The [Great Expectations Data Validator](../data-validators/great-expectations.md) is such an example.
+In addition to pipeline artifacts, the Artifact Store may also be used as storage backed by other specialized stack components that need to store their data in the form of persistent object storage. The [Great Expectations Data Validator](https://docs.zenml.io/stacks/data-validators/great-expectations) is such an example.
 
 Related concepts:
 

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

@@ -76,9 +76,9 @@ Certain dashboard functionality, such as visualizing or deleting artifacts, is n
 
 The implicit authentication method also needs to be coordinated with other stack components that are highly dependent on the Artifact Store and need to interact with it directly to the function. If these components are not running on your machine, they do not have access to the local environment variables and will encounter authentication failures while trying to access the Azure Artifact Store:
 
-* [Orchestrators](../orchestrators/) need to access the Artifact Store to manage pipeline artifacts
-* [Step Operators](../step-operators/) need to access the Artifact Store to manage step-level artifacts
-* [Model Deployers](../model-deployers/) need to access the Artifact Store to load served models
+* [Orchestrators](https://docs.zenml.io/stacks/orchestrators/) need to access the Artifact Store to manage pipeline artifacts
+* [Step Operators](https://docs.zenml.io/stacks/step-operators/) need to access the Artifact Store to manage step-level artifacts
+* [Model Deployers](https://docs.zenml.io/stacks/model-deployers/) need to access the Artifact Store to load served models
 
 To enable these use cases, it is recommended to use [an Azure Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/azure-service-connector) to link your Azure Artifact Store to the remote Azure Blob storage container.
 {% endhint %}

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

@@ -62,9 +62,9 @@ Certain dashboard functionality, such as visualizing or deleting artifacts, is n
 
 The implicit authentication method also needs to be coordinated with other stack components that are highly dependent on the Artifact Store and need to interact with it directly to the function. If these components are not running on your machine, they do not have access to the local Google Cloud CLI configuration and will encounter authentication failures while trying to access the GCS Artifact Store:
 
-* [Orchestrators](../orchestrators/) need to access the Artifact Store to manage pipeline artifacts
-* [Step Operators](../step-operators/) need to access the Artifact Store to manage step-level artifacts
-* [Model Deployers](../model-deployers/) need to access the Artifact Store to load served models
+* [Orchestrators](https://docs.zenml.io/stacks/orchestrators/) need to access the Artifact Store to manage pipeline artifacts
+* [Step Operators](https://docs.zenml.io/stacks/step-operators/) need to access the Artifact Store to manage step-level artifacts
+* [Model Deployers](https://docs.zenml.io/stacks/model-deployers/) need to access the Artifact Store to load served models
 
 To enable these use cases, it is recommended to use [a GCP Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/gcp-service-connector) to link your GCS Artifact Store to the remote GCS bucket.
 {% endhint %}

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

@@ -17,9 +17,9 @@ Furthermore, the local Artifact Store doesn't cover services like high-availabil
 
 The fact that it stores artifacts on your local filesystem also means that not all stack components can be used in the same stack as a local Artifact Store:
 
-* only [Orchestrators](../orchestrators/) running on the local machine, such as the [local Orchestrator](../orchestrators/local.md), a [local Kubeflow Orchestrator](../orchestrators/kubeflow.md), or a [local Kubernetes Orchestrator](../orchestrators/kubernetes.md) can be combined with a local Artifact Store
-* only [Model Deployers](../model-deployers/) that are running locally, such as the [MLflow Model Deployer](../model-deployers/mlflow.md), can be used in combination with a local Artifact Store
-* [Step Operators](../step-operators/): none of the Step Operators can be used in the same stack as a local Artifact Store, given that their very purpose is to run ZenML steps in remote specialized environments
+* only [Orchestrators](https://docs.zenml.io/stacks/orchestrators/) running on the local machine, such as the [local Orchestrator](https://docs.zenml.io/stacks/orchestrators/local), a [local Kubeflow Orchestrator](https://docs.zenml.io/stacks/orchestrators/kubeflow), or a [local Kubernetes Orchestrator](https://docs.zenml.io/stacks/orchestrators/kubernetes) can be combined with a local Artifact Store
+* only [Model Deployers](https://docs.zenml.io/stacks/model-deployers/) that are running locally, such as the [MLflow Model Deployer](https://docs.zenml.io/stacks/model-deployers/mlflow), can be used in combination with a local Artifact Store
+* [Step Operators](https://docs.zenml.io/stacks/step-operators/): none of the Step Operators can be used in the same stack as a local Artifact Store, given that their very purpose is to run ZenML steps in remote specialized environments
 
 As you transition to a team setting or a production setting, you can replace the local Artifact Store in your stack with one of the other flavors that are better suited for these purposes, with no changes required in your code.
 {% endhint %}

docs/book/component-guide/artifact-stores/s3.md

@@ -62,9 +62,9 @@ Certain dashboard functionality, such as visualizing or deleting artifacts, is n
 
 The implicit authentication method also needs to be coordinated with other stack components that are highly dependent on the Artifact Store and need to interact with it directly to work. If these components are not running on your machine, they do not have access to the local AWS CLI configuration and will encounter authentication failures while trying to access the S3 Artifact Store:
 
-* [Orchestrators](../orchestrators/) need to access the Artifact Store to manage pipeline artifacts
-* [Step Operators](../step-operators/) need to access the Artifact Store to manage step-level artifacts
-* [Model Deployers](../model-deployers/) need to access the Artifact Store to load served models
+* [Orchestrators](https://docs.zenml.io/stacks/orchestrators/) need to access the Artifact Store to manage pipeline artifacts
+* [Step Operators](https://docs.zenml.io/stacks/step-operators/) need to access the Artifact Store to manage step-level artifacts
+* [Model Deployers](https://docs.zenml.io/stacks/model-deployers/) need to access the Artifact Store to load served models
 
 To enable these use-cases, it is recommended to use [an AWS Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/aws-service-connector) to link your S3 Artifact Store to the remote S3 bucket.
 {% endhint %}

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

@@ -12,9 +12,9 @@ environment that allows code to run in an isolated manner.
 ### When to use it
 
 The container registry is needed whenever other components of your stack need to push or pull container images.\
-Currently, this is the case for most of ZenML's remote [orchestrators](../orchestrators/)\
-, [step operators](../step-operators/), and\
-some [model deployers](../model-deployers/). These containerize your pipeline code and therefore\
+Currently, this is the case for most of ZenML's remote [orchestrators](https://docs.zenml.io/stacks/orchestrators/)\
+, [step operators](https://docs.zenml.io/stacks/step-operators/), and\
+some [model deployers](https://docs.zenml.io/stacks/model-deployers/). These containerize your pipeline code and therefore\
 require a container registry to store the resulting [Docker](https://www.docker.com/) images. Take a look at the\
 documentation page of the component you want to use in your stack to see if it requires a container registry or even a\
 specific container registry flavor.

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

@@ -28,7 +28,7 @@ The ECR registry is automatically activated once you create an AWS account. Howe
 * Go to the [ECR website](https://console.aws.amazon.com/ecr).
 * Make sure the correct region is selected on the top right.
 * Click on `Create repository`.
-* Create a private repository. The name of the repository depends on the [orchestrator](../orchestrators/) or [step operator](../step-operators/) you're using in your stack.
+* Create a private repository. The name of the repository depends on the [orchestrator](https://docs.zenml.io/stacks/orchestrators/) or [step operator](https://docs.zenml.io/stacks/step-operators/) you're using in your stack.
 
 ### URI format
 

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

@@ -17,7 +17,7 @@ You should use the DockerHub container registry if:
 
 To use the DockerHub container registry, all you need to do is create a [DockerHub](https://hub.docker.com/) account.
 
-When this container registry is used in a ZenML stack, the Docker images that are built will be published in a \*\* public\*\* repository and everyone will be able to pull your images. If you want to use a **private** repository instead, you'll have to [create a private repository](https://docs.docker.com/docker-hub/repos/#creating-repositories) on the website before running the pipeline. The repository name depends on the remote [orchestrator](../orchestrators/) or [step operator](../step-operators/) that you're using in your stack.
+When this container registry is used in a ZenML stack, the Docker images that are built will be published in a \*\* public\*\* repository and everyone will be able to pull your images. If you want to use a **private** repository instead, you'll have to [create a private repository](https://docs.docker.com/docker-hub/repos/#creating-repositories) on the website before running the pipeline. The repository name depends on the remote [orchestrator](https://docs.zenml.io/stacks/orchestrators/) or [step operator](https://docs.zenml.io/stacks/step-operators/) that you're using in your stack.
 
 ### How to find the registry URI
 

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

@@ -14,7 +14,7 @@ Data profiling, data integrity testing, data and model drift detection are all w
 Related concepts:
 
 * the Data Validator is an optional type of Stack Component that needs to be registered as part of your ZenML [Stack](https://docs.zenml.io/user-guides/production-guide/understand-stacks).
-* Data Validators used in ZenML pipelines usually generate data profiles and data quality check reports that are versioned and stored in the [Artifact Store](../artifact-stores/) and can be [retrieved and visualized](https://docs.zenml.io/how-to/data-artifact-management/visualize-artifacts/) later.
+* Data Validators used in ZenML pipelines usually generate data profiles and data quality check reports that are versioned and stored in the [Artifact Store](https://docs.zenml.io/stacks/artifact-stores/) and can be [retrieved and visualized](https://docs.zenml.io/how-to/data-artifact-management/visualize-artifacts/) later.
 
 ### When to use it