Improve and restructure deployment docs

docs/book/getting-started/core-concepts.md

@@ -133,7 +133,7 @@ To use _stack components_ that are running remotely on a cloud infrastructure, y
 
 #### Server Deployment
 
-In order to benefit from the advantages of using a deployed ZenML server, you can either choose to use the [**ZenML Pro SaaS offering**](zenml-pro/zenml-pro.md) which provides a control plane for you to create managed instances of ZenML servers, or [deploy it in your self-hosted environment](deploying-zenml/README.md).
+In order to benefit from the advantages of using a deployed ZenML server, you can either choose to use the [**ZenML Pro SaaS offering**](zenml-pro.md) which provides a control plane for you to create managed instances of ZenML servers, or [deploy it in your self-hosted environment](deploying-zenml/README.md).
 
 #### Metadata Tracking
 

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

@@ -10,32 +10,61 @@ Moving your ZenML Server to a production environment offers several benefits ove
 2. **Reliability**: Production-grade infrastructure ensures high availability and fault tolerance, minimizing downtime and ensuring consistent performance.
 3. **Collaboration**: A shared production environment enables seamless collaboration between team members, making it easier to iterate on models and share insights.
 
-Despite these advantages, transitioning to production can be challenging due to the complexities involved in setting up the needed infrastructure.
+Despite these advantages, transitioning to production can be challenging due to
+the complexities involved in setting up the needed infrastructure.
+
+## Components
 
 ### ZenML Server
 
-When you first get started with ZenML, it relies with the following architecture on your machine.
+When you first get started with ZenML, you have the following architecture on your machine.
 
-![ZenML default local configuration](../../.gitbook/assets/Scenario1.png)
+![ZenML default local configuration](../.gitbook/assets/Scenario1.png)
 
-The SQLite database that you can see in this diagram is used to store information about pipelines, pipeline runs, stacks, and other configurations. Users can run the `zenml up` command to spin up a local REST server to serve the dashboard. The diagram for this looks as follows:
+The SQLite database that you can see in this diagram is used to store
+information about pipelines, pipeline runs, stacks, and other configurations.
+This default setup allows you to get started and try out the core features but
+you won't be able to use cloud-based components like serverless orchestrators
+and so on.
 
-![ZenML with a local REST Server](../../.gitbook/assets/Scenario2.png)
+Users can run the `zenml up` command to spin up a local REST server to serve the
+dashboard. For the local REST server option, the `zenml up` command implicitly connects the client to the server. The diagram for this looks as follows:
 
-{% hint style="info" %}
-For the local REST server option, the `zenml up` command implicitly connects the client to the server.
-{% endhint %}
+![ZenML with a local REST Server](../.gitbook/assets/Scenario2.png)
 
 {% hint style="warning" %}
-Currently the ZenML server supports a legacy and a brand-new version of the dashboard. To use the legacy version simply use the
-following command `zenml up --legacy`
+Currently the ZenML server supports two versions of the dashboard: a legacy view
+and a brand-new view. To use the legacy version simply use the following
+command: `zenml up --legacy`.
 {% endhint %}
 
 In order to move into production, the ZenML server needs to be deployed somewhere centrally so that the different cloud stack components can read from and write to the server. Additionally, this also allows all your team members to connect to it and share stacks and pipelines.
 
-![Deployed ZenML Server](../../.gitbook/assets/Scenario3.2.png)
+![Deployed ZenML Server](../.gitbook/assets/Scenario3.2.png)
+
+You connect to your deployed ZenML server using the `zenml connect` command and
+then you have the full benefits and power of ZenML. You can use all the
+cloud-based components, your metadata will be stored and synchronized across all
+the users of the server and you can leverage features like centralized logs
+storage and pipeline artifact visualization.
+
+### ZenML Client
+
+The ZenML client is a Python package that you can install on your machine. It
+is used to interact with the ZenML server. You can install it using the `pip`
+command. This Python package gives you [the `zenml` command-line interface](https://sdkdocs.zenml.io/latest/cli/) which
+you can use to interact with the ZenML server for common tasks like managing
+stacks, setting up secrets, and so on.
+
+If you want to have more fine-grained control and access to the metadata that
+ZenML manages, you can use the Python SDK to access the API. This allows you to
+create your own custom automations and scripts and is the most common way teams
+access the metadata stored in the ZenML server. Our full documentation for the
+Python SDK can be found [here](https://sdkdocs.zenml.io/latest/). Our full HTTP
+API documentation can also be found by adding the `/doc` suffix to the URL when
+accessing your deployed ZenML server.
 
-### Deploying a ZenML Server
+## Deploying a ZenML Server
 
 Deploying the ZenML Server is a crucial step towards transitioning to a production-grade environment for your machine learning projects. By setting up a deployed ZenML Server instance, you gain access to powerful features, allowing you to use stacks with remote components, centrally track progress, collaborate effectively, and achieve reproducible results.
 
@@ -50,7 +79,7 @@ Currently the ZenML server supports a legacy and a brand-new version of the dash
 
 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.
 
-## How to deploy ZenML
+### How to deploy ZenML
 
 Documentation for the various deployment strategies can be found in the following pages below (in our 'how-to' guides):
 

docs/book/getting-started/zenml-pro/system-architectures.md → docs/book/getting-started/system-architectures.md

@@ -16,7 +16,7 @@ productivity. No matter your specific needs, the hosting options for ZenML Pro
 range from easy SaaS integration to completely airgapped deployments on your own
 infrastructure.
 
-A ZenML Pro deployment consists of the following moving pieces for both the SaaS
+A [ZenML Pro deployment](./zenml-pro.md) consists of the following moving pieces for both the SaaS
 product as well as the self-hosted version.:
 
 * **ZenML Pro Control Plane**: This is a centralized MLOps control plane that includes a
@@ -35,12 +35,14 @@ product as well as the self-hosted version.:
   ML metadata such as tracking and versioning information about pipelines and
   models.
 
+## Deployment Scenarios
+
 The above four interact with other MLOps stack components, secrets, and data in
-varying scenarios described below.
+the two scenarios described below.
 
-## Scenario 1: Full SaaS
+### Scenario 1: Full SaaS
 
-![Scenario 1: Full SaaS deployment](../../.gitbook/assets/cloud_architecture_scenario_1.png)
+![Scenario 1: Full SaaS deployment](../.gitbook/assets/cloud_architecture_scenario_1.png)
 
 
 In this scenario, all services are hosted on infrastructure hosted by the ZenML Team,
@@ -53,7 +55,7 @@ model tracking and versioning information) is stored. All the actual ML data
 artifacts (e.g. data produced or consumed by pipeline steps, logs and
 visualizations, models) are stored on the customer cloud. This can be set up
 quite easily by configuring
-an [artifact store](../../component-guide/artifact-stores/artifact-stores.md)
+an [artifact store](../component-guide/artifact-stores/artifact-stores.md)
 with your MLOps stack.
 
 Your tenant only needs permissions to read from this data to display artifacts
@@ -66,12 +68,9 @@ This scenario is meant for customers who want to quickly get started with ZenML
 and can to a certain extent allow ingress connections into their infrastructure
 from an external SaaS provider.
 
-## Scenario 2: Hybrid SaaS with Customer Secret Store managed by ZenML
-
-![Scenario 2: Hybrid SaaS with Customer Secret Store managed by ZenML](../../.gitbook/assets/cloud_architecture_scenario_2.png)
-
-This scenario is a version of Scenario 1. modified to store all sensitive
-information on the customer side. In this case, the customer connects their own
+{% hint style="info" %}
+We also offer a hybrid SaaS option where customer secrets are stored on the
+customer side. In this case, the customer connects their own
 secret store directly to the ZenML server that is managed by us. All ZenML
 secrets used by running pipelines to access infrastructure services and
 resources are stored in the customer secret store. This allows users to
@@ -80,23 +79,11 @@ and the [secrets API](../../how-to/interact-with-secrets.md) to authenticate
 ZenML pipelines and the ZenML Pro to 3rd party services and infrastructure
 while ensuring that credentials are always stored on the customer side.
 
-Even though they are stored customer side, access to ZenML secrets is fully
-managed by ZenML Pro. The individually deployed ZenML Servers can also allowed to use some of those
-credentials to connect directly to customer infrastructure services to implement
-control plane features such as artifact visualization or triggering pipelines.
-This implies that the secret values are allowed to leave the customer
-environment to allow their access to be managed centrally by the ZenML Pro and
-to enforce access control policies, but the ZenML users and pipelines never have
-direct access to the secret store.
-
-All access to customer secrets is, of course, regulated through authentication
-and RBAC, so that only authorized users can access the secrets. This deployment
-scenario is meant for customers who want to use the ZenML Pro but want to keep
-their secrets on their own infrastructure.
+{% endhint %}
 
-## Scenario 3: Fully On-prem
+### Scenario 2: Fully On-prem
 
-![Scenario 3: Fully on-premises deployment](../../.gitbook/assets/cloud_architecture_scenario_5.png)
+![Scenario 2: Fully on-premises deployment](../.gitbook/assets/cloud_architecture_scenario_2.png)
 
 In this scenario, all services, data, and secrets are deployed on the customer
 cloud. This is the opposite of Scenario 1, and is meant for customers who
@@ -106,4 +93,10 @@ require completely airgapped deployments, for the tightest security standards.
 Are you interested in ZenML Pro? [Sign up](https://cloud.zenml.io/?utm\_source=docs\&utm\_medium=referral\_link\&utm\_campaign=cloud\_promotion\&utm\_content=signup\_link)
 and get access to Scenario 1. with a free 14 day trial now!
 
+## ZenML Pro vs ZenML Open Source
+
+TODO: add diagram + feature differences
+also more on architectural distinctions
+
+
 <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/zenml-pro.md

@@ -0,0 +1,84 @@
+# ☁️ ZenML Pro
+
+The Pro version of ZenML comes with a number of features that expand the
+functionality of the Open Source product
+
+### Role-based access control and permissions
+
+Utilizing ZenML Pro provides you with access to a robust control plane that
+unifies user management and optimizes your workflows. Efficiently manage access
+and permissions through centralized user administration. Create fine-grained
+permissions for resources such as stacks, pipelines, models, etc.
+
+See the section on [user management](../how-to/use-zenml-pro/user-management.md) to learn more.
+
+### A brand-new, modern MLOps experience
+
+![Walkthrough of ZenML Model Control Plane](../.gitbook/assets/mcp_walkthrough.gif)
+
+We have built the ZenML Pro experience from the ground-up. With ZenML Pro, you get
+access to a new dashboard, with a better experience. The new dashboard features
+more functionality such as
+the [Model Control Plane](../user-guide/starter-guide/track-ml-models.md)
+and [Artifact Control Plane](../user-guide/starter-guide/manage-artifacts.md).
+
+### Run templates for running your pipelines from the dashboard or the API
+
+ZenML Pro enables you to [create and run templates](../how-to/trigger-pipelines/README.md#run-templates).
+This way, you can use the dashboard or our Client/REST API to run a pipeline with updated configuration
+which allows you to iterate quickly with minimal friction. 
+
+### Triggers, CI/CD, Reports and more
+
+Additionally, ZenML Pro users get exclusive access to an array of
+cloud-specific features, such as triggers, integrating with your code
+repository CI/CD system, generating usage reports and more.
+
+### Managed ZenML Server (Multi-tenancy)
+
+ZenML Pro (SaaS solution) simplifies your machine learning workflows, enabling you to deploy a
+managed instance of ZenML servers with just one click. This eradicates the need
+to handle infrastructure complexities, making the set-up and management of your
+machine learning pipelines a breeze. We handle all pertinent system updates and
+backups, thus ensuring your system stays current and robust, allowing you to
+zero in on your essential MLOps tasks. As a ZenML Pro user, you'll also have
+priority support, giving you the necessary aid to fully utilize the platform.
+
+### Maximum data security
+
+At ZenML Pro, your data security and privacy are our top priority. The
+platform enables a secure connection to your infrastructure, tracking only
+metadata via an encrypted connection to maintain the confidentiality of your
+sensitive information. ZenML Pro integrates smoothly with your cloud services
+via service connectors, allowing a straightforward connection with various cloud
+resources without sacrificing data security. We hold your confidential
+information in a secure and isolated environment, offering an extra degree of
+protection. If desired, you can
+even [supply your own secret store](../how-to/interact-with-secrets/use-a-custom-secret-store.md).
+
+Learn more about ZenML Pro on the [ZenML Website](https://zenml.io/pro).
+
+## Managed ZenML (SaaS)
+
+One of the most straightforward paths to start with a deployed ZenML server is
+to use [ZenML Pro](https://zenml.io/pro). The ZenML Pro offering eliminates the need
+for you to dedicate time and resources to deploy and manage a ZenML server,
+allowing you to focus primarily on your MLOps workflows.
+
+<div data-full-width="false">
+
+<figure>.gitbook/assets/zenml-cloud-tenant-overview.png" alt=""><figcaption><p>ZenML Pro comes equipped with powerful pro-only dashboard features</p></figcaption></figure>
+
+</div>
+
+{% hint style="info" %}
+If you're interested in assessing ZenML Pro, you can simply create
+a [free account](https://cloud.zenml.io/?utm\_source=docs\&utm\_medium=referral\_link\&utm\_campaign=cloud\_promotion\&utm\_content=signup\_link).
+Learn more about ZenML Pro on the [ZenML Website](https://zenml.io/pro).
+{% endhint %}
+
+Click [here](./system-architectures.md) to understand about the ZenML Pro system
+architecture.
+
+<!-- For scarf -->
+<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>