Fixing static pro links

Author: strickvl

docs/book/getting-started/deploying-zenml/README.md

@@ -84,7 +84,7 @@ Deploying the ZenML Server is a crucial step towards transitioning to a producti
 
 Currently, there are two main options to access a deployed ZenML server:
 
-1. **Managed deployment:** With [ZenML Pro](../zenml-pro/README.md) offering you can utilize a control plane to create ZenML servers, also known as [workspaces](../zenml-pro/workspaces.md). These workspaces are managed and maintained by ZenML's dedicated team, alleviating the burden of server management from your end. Importantly, your data remains securely within your stack, and ZenML's role is primarily to handle tracking of metadata and server maintenance.
+1. **Managed deployment:** With [ZenML Pro](https://docs.zenml.io/pro) offering you can utilize a control plane to create ZenML servers, also known as [workspaces](https://docs.zenml.io/pro/core-concepts/workspaces). These workspaces are managed and maintained by ZenML's dedicated team, alleviating the burden of server management from your end. Importantly, your data remains securely within your stack, and ZenML's role is primarily to handle tracking of metadata and server maintenance.
 2. **Self-hosted Deployment:** Alternatively, you have the ability to deploy ZenML on your own self-hosted environment. This can be achieved through various methods, including using [Docker](./deploy-with-docker.md), [Helm](./deploy-with-helm.md), or [HuggingFace Spaces](./deploy-using-huggingface-spaces.md). We also offer our Pro version for self-hosted deployments, so you can use our full paid feature-set while staying fully in control with an air-gapped solution on your infrastructure.
 
 Both options offer distinct advantages, allowing you to choose the deployment approach that best aligns with your organization's needs and infrastructure preferences. Whichever path you select, ZenML facilitates a seamless and efficient way to take advantage of the ZenML Server and enhance your machine learning workflows for production-level success.
@@ -93,6 +93,6 @@ Both options offer distinct advantages, allowing you to choose the deployment ap
 
 Documentation for the various deployment strategies can be found in the following pages below (in our 'how-to' guides):
 
-<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden></th><th data-hidden data-type="content-ref"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><mark style="color:purple;"><strong>Deploying ZenML using ZenML Pro</strong></mark></td><td>Deploying ZenML using ZenML Pro.</td><td></td><td></td><td><a href="../zenml-pro/README.md">deploy-with-zenml-cli.md</a></td></tr><tr><td><mark style="color:purple;"><strong>Deploy with Docker</strong></mark></td><td>Deploying ZenML in a Docker container.</td><td></td><td></td><td><a href="./deploy-with-docker.md">deploy-with-docker.md</a></td></tr><tr><td><mark style="color:purple;"><strong>Deploy with Helm</strong></mark></td><td>Deploying ZenML in a Kubernetes cluster with Helm.</td><td></td><td></td><td><a href="./deploy-with-helm.md">deploy-with-helm.md</a></td></tr><tr><td><mark style="color:purple;"><strong>Deploy with HuggingFace Spaces</strong></mark></td><td>Deploying ZenML to Hugging Face Spaces.</td><td></td><td></td><td><a href="./deploy-using-huggingface-spaces.md">deploy-with-hugging-face-spaces.md</a></td></tr></tbody></table>
+<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden></th><th data-hidden data-type="content-ref"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><mark style="color:purple;"><strong>Deploying ZenML using ZenML Pro</strong></mark></td><td>Deploying ZenML using ZenML Pro.</td><td></td><td></td><td><a href="https://docs.zenml.io/pro">deploy-with-zenml-cli.md</a></td></tr><tr><td><mark style="color:purple;"><strong>Deploy with Docker</strong></mark></td><td>Deploying ZenML in a Docker container.</td><td></td><td></td><td><a href="./deploy-with-docker.md">deploy-with-docker.md</a></td></tr><tr><td><mark style="color:purple;"><strong>Deploy with Helm</strong></mark></td><td>Deploying ZenML in a Kubernetes cluster with Helm.</td><td></td><td></td><td><a href="./deploy-with-helm.md">deploy-with-helm.md</a></td></tr><tr><td><mark style="color:purple;"><strong>Deploy with HuggingFace Spaces</strong></mark></td><td>Deploying ZenML to Hugging Face Spaces.</td><td></td><td></td><td><a href="./deploy-using-huggingface-spaces.md">deploy-with-hugging-face-spaces.md</a></td></tr></tbody></table>
 
 <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/system-architectures.md

@@ -79,7 +79,7 @@ The above components interact with other MLOps stack components, secrets, and da
 the following scenarios described below.
 
 {% hint style="info" %}
-To learn more about the core concepts for ZenML Pro, go [here](../getting-started/zenml-pro/core-concepts.md)
+To learn more about the core concepts for ZenML Pro, go [here](https://docs.zenml.io/pro/core-concepts)
 {% endhint %}
 
 ### ZenML Pro SaaS Architecture

docs/book/how-to/project-setup-and-management/collaborate-with-team/access-management.md

@@ -51,7 +51,7 @@ Learn more about the best practices in managing credentials and recommended role
 The decision to upgrade your ZenML server is usually taken by your Project Owners after consulting with all the teams using the server. This is because there might be teams with conflicting requirements and moving to a new version of ZenML (that might come with upgrades to certain libraries) can break code for some users.
 
 {% hint style="info" %}
-You can choose to have different servers for different teams and that can alleviate some of the pressure to upgrade if you have multiple teams using the same server. ZenML Pro offers [multi-tenancy](https://docs.zenml.io/getting-started/zenml-pro/workspaces) out of the box, for situations like these. Sign up for a free trial to try it yourself: https://cloud.zenml.io/
+You can choose to have different servers for different teams and that can alleviate some of the pressure to upgrade if you have multiple teams using the same server. ZenML Pro offers [multi-tenancy](https://docs.zenml.io/pro/core-concepts/workspaces) out of the box, for situations like these. Sign up for a free trial to try it yourself: https://cloud.zenml.io/
 {% endhint %}
 
 Performing the upgrade itself is a task that typically falls on the MLOps Platform Engineers. They should:

docs/book/reference/api-reference.md

@@ -55,7 +55,7 @@ curl -H "Authorization: Bearer YOUR_API_TOKEN" https://cloudapi.zenml.io/users/m
 
 Note that for workspace programmatic access, you can use the same methods described below for the OSS API (temporary API tokens or service accounts).
 
-For full details on using the ZenML Pro API, including available endpoints and features, see the [Pro API guide](../getting-started/zenml-pro/pro-api.md).
+For full details on using the ZenML Pro API, including available endpoints and features, see the [Pro API guide](https://docs.zenml.io/pro/deployments/pro-api).
 
 ## Accessing the ZenML OSS API