fix more links

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/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/how-to/pipeline-development/build-pipelines/fetching-pipelines.md

@@ -260,7 +260,7 @@ output.visualize()
 ![output.visualize() Output](../../../.gitbook/assets/artifact\_visualization\_evidently.png)
 
 {% hint style="info" %}
-If you're not in a Jupyter notebook, you can simply view the visualizations in the ZenML dashboard by running `zenml login --local` and clicking on the respective artifact in the pipeline run DAG instead. Check out the [artifact visualization page](../../handle-data-artifacts/visualize-artifacts.md) to learn more about how to build and view artifact visualizations in ZenML!
+If you're not in a Jupyter notebook, you can simply view the visualizations in the ZenML dashboard by running `zenml login --local` and clicking on the respective artifact in the pipeline run DAG instead. Check out the [artifact visualization page](../../../how-to/data-artifact-management/visualize-artifacts/README.md) to learn more about how to build and view artifact visualizations in ZenML!
 {% endhint %}
 
 ## Fetching information during run execution

docs/book/how-to/pipeline-development/build-pipelines/use-failure-success-hooks.md

@@ -121,7 +121,7 @@ def my_step(some_parameter: int = 1)
 
 <summary>See it in action with the E2E example</summary>
 
-_To set up the local environment used below, follow the recommendations from the_ [_Project templates_](../../project-setup-and-management/setting-up-a-project-repository/using-project-templates.md)_._
+_To set up the local environment used below, follow the recommendations from the_ [_Project templates_](../../../how-to/project-setup-and-management/setting-up-a-project-repository/using-project-templates.md)_._
 
 In [`steps/alerts/notify_on.py`](../../../../examples/e2e/steps/alerts/notify_on.py), you will find a step to notify the user about success and a function used to notify the user about step failure using the [Alerter](../../../component-guide/alerters/alerters.md) from the active stack.
 
@@ -188,7 +188,7 @@ def my_step(...):
 
 <summary>See it in action with the E2E example</summary>
 
-_To set up the local environment used below, follow the recommendations from the_ [_Project templates_](../../project-setup-and-management/setting-up-a-project-repository/using-project-templates.md)_._
+_To set up the local environment used below, follow the recommendations from the_ [_Project templates_](../../../how-to/project-setup-and-management/setting-up-a-project-repository/using-project-templates.md)_._
 
 In [`steps/alerts/notify_on.py`](../../../../examples/e2e/steps/alerts/notify_on.py), you will find a step to notify the user about success and a function used to notify the user about step failure using the [Alerter](../../../component-guide/alerters/alerters.md) from the active stack.
 

docs/book/how-to/pipeline-development/training-with-gpus/README.md

@@ -56,7 +56,7 @@ All steps running on GPU-backed hardware will be executed within a containerized
 
 #### 1. **Specify a CUDA-enabled parent image in your `DockerSettings`**
 
-For complete details, refer to the [containerization page](../../infrastructure-deployment/customize-docker-builds/README.md) that explains how to do this. As an example, if you want to use the latest CUDA-enabled official PyTorch image for your entire pipeline run, you can include the following code:
+For complete details, refer to the [containerization page](../../../how-to/customize-docker-builds/README.md) that explains how to do this. As an example, if you want to use the latest CUDA-enabled official PyTorch image for your entire pipeline run, you can include the following code:
 
 ```python
 from zenml import pipeline

docs/book/user-guide/production-guide/deploying-zenml.md

@@ -47,7 +47,7 @@ zenml login <server-url>
 ```
 
 {% hint style="info" %}
-Having trouble connecting with a browser? There are other ways to connect. Read [here](../../how-to/connecting-to-zenml/README.md) for more details.
+Having trouble connecting with a browser? There are other ways to connect. Read [here](../../how-to/manage-zenml-server/connecting-to-zenml/README.md) for more details.
 {% endhint %}
 
 This command will start a series of steps to validate the device from where you are connecting that will happen in your browser. After that, you're now locally connected to a remote ZenML. Nothing of your experience changes, except that all metadata that you produce will be tracked centrally in one place from now on.