Some important docs updates

README.md

@@ -68,10 +68,11 @@
 [build-url]: https://github.com/zenml-io/zenml/actions/workflows/ci.yml
 
 <div align="center">
+  <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=0fcbab94-8fbe-4a38-93e8-c2348450a42e" />
   <h3 align="center">Build portable, production-ready MLOps pipelines.</h3>
   <p align="center">
     <div align="center">
-      Join our <a href="https://zenml.io/slack-invite" target="_blank">
+      Join our <a href="https://zenml.io/slack" target="_blank">
       <img width="18" src="https://cdn3.iconfinder.com/data/icons/logos-and-brands-adobe/512/306_Slack-512.png" alt="Slack"/>
     <b>Slack Community</b> </a> and be part of the ZenML family.
     </div>
@@ -88,7 +89,7 @@
     ·
     <a href="https://github.com/issues?q=is%3Aopen+is%3Aissue+archived%3Afalse+user%3Azenml-io+label%3A%22good+first+issue%22">Contribute to Open Source</a>
     ·
-    <a href="https://www.zenml.io/company#team">Meet the Team</a>
+    <a href="https://github.com/zenml-io/zenml-projects">Projects Showcase</a>
     <br />
     <br />
     🎉 Version 0.55.3 is out. Check out the release notes
@@ -104,21 +105,15 @@
 <details>
   <summary>🏁 Table of Contents</summary>
   <ol>
-    <li><a href="#-introduction">Introduction</a></li>
-    <li><a href="#-quickstart">Quickstart</a></li>
+    <li><a href="#🤖-introduction">Introduction</a></li>
+    <li><a href="#🤸-quickstart">Quickstart</a></li>
     <li>
-      <a href="#-create-your-own-mlops-platform">Create your own MLOps Platform</a>
-      <ul>
-        <li><a href="##-1-deploy-zenml">Deploy ZenML</a></li>
-        <li><a href="#-2-deploy-stack-components">Deploy Stack Components</a></li>
-        <li><a href="#-3-create-a-pipeline">Create a Pipeline</a></li>
-        <li><a href="#-4-start-the-dashboard">Start the Dashboard</a></li>
-      </ul>
+      <a href="#🖼️-learning">Learning</a>
     </li>
-    <li><a href="#-roadmap">Roadmap</a></li>
-    <li><a href="#-contributing-and-community">Contributing and Community</a></li>
-    <li><a href="#-getting-help">Getting Help</a></li>
-    <li><a href="#-license">License</a></li>
+    <li><a href="#🗺-roadmap">Roadmap</a></li>
+    <li><a href="#🙌-contributing-and-community">Contributing and Community</a></li>
+    <li><a href="#🆘-getting-help">Getting Help</a></li>
+    <li><a href="#📜-license">License</a></li>
   </ol>
 </details>
 
@@ -141,17 +136,14 @@ experimentation while writing code that is production-ready from the get-go.
 - 🛫 ZenML enables MLOps infrastructure experts to define, deploy, and manage
 sophisticated production environments that are easy to use for colleagues.
 
-![The long journey from experimentation to production.](/docs/book/.gitbook/assets/intro-zenml-overview.png)
-
-ZenML provides a user-friendly syntax designed for ML workflows, compatible with
-any cloud or tool. It enables centralized pipeline management, enabling
-developers to write code once and effortlessly deploy it to various
-infrastructures.
-
 <div align="center">
-    <img src="docs/book/.gitbook/assets/overview.gif">
+  <img width="60%" src="/docs/book/.gitbook/assets/zenml-hero.png" alt="ZenML Hero"/>
 </div>
 
+ZenML aims to optimize your entire machine learning lifecycle - you'll be 80% faster to production, generate 3x more models, and massively reduce your cloud spend.
+
+![The long journey from experimentation to production.](/docs/book/.gitbook/assets/zenml-why.png)
+
 # 🤸 Quickstart
 
 [Install ZenML](https://docs.zenml.io/getting-started/installation) via
@@ -167,113 +159,31 @@ Take a tour with the guided quickstart by running:
 zenml go
 ```
 
-# 🖼️ Create your own MLOps Platform
-
-ZenML allows you to create and manage your own MLOps platform using 
-best-in-class open-source and cloud-based technologies. Here is an example of 
-how you could set this up for your team:
-
-## 🔋 1. Deploy ZenML
-
-For full functionality ZenML should be deployed on the cloud to
-enable collaborative features as the central MLOps interface for teams.
-
-![ZenML Architecture Diagram.](docs/book/.gitbook/assets/Scenario3.png)
-
-Currently, there are two main options to deploy ZenML:
-
-- **ZenML Cloud**: With [ZenML Cloud](https://docs.zenml.io/deploying-zenml/zenml-cloud), 
-you can utilize a control plane to create ZenML servers, also known as tenants. 
-These tenants are managed and maintained by ZenML's dedicated team, alleviating 
-the burden of server management from your end. 
-
-- **Self-hosted deployment**: Alternatively, you have the flexibility to [deploy 
-ZenML on your own self-hosted environment](https://docs.zenml.io/deploying-zenml/zenml-self-hosted). 
-This can be achieved through various methods, including using our CLI, Docker, 
-Helm, or HuggingFace Spaces.
-
-## 👨‍🍳 2. Deploy Stack Components
-
-ZenML boasts a ton of [integrations](https://zenml.io/integrations) into 
-popular MLOps tools. The [ZenML Stack](https://docs.zenml.io/user-guide/starter-guide/understand-stacks) 
-concept ensures that these tools work nicely together, therefore bringing
-structure and standardization into the MLOps workflow.
-
-Deploying and configuring this is super easy with ZenML. For **AWS**, this might 
-look a bit like this
-
-```bash
-# Deploy and register an orchestrator and an artifact store
-zenml orchestrator deploy kubernetes_orchestrator --flavor kubernetes --cloud aws
-zenml artifact-store deploy s3_artifact_store --flavor s3
-
-# Register this combination of components as a stack
-zenml stack register production_stack --orchestrator kubernetes_orchestrator --artifact-store s3_artifact_store --set # Register your production environment
-```
-
-When you run a pipeline with this stack set, it will be running on your deployed
-Kubernetes cluster.
+# 🖼️ Learning
 
-You can also [deploy your own tooling manually](https://docs.zenml.io/stacks-and-components/stack-deployment).
+The best way to learn about ZenML is the [docs](https://docs.zenml.io) - with the best path being through the [Starter Guide](https://docs.zenml.io/user-guide/starter-guide).
 
-## 🏇 3. Create a Pipeline
+For inspiration, here are some other examples & use-cases:
 
-Here's an example of a hello world ZenML pipeline in code:
-
-```python
-# run.py
-from zenml import pipeline, step
-
-
-@step
-def step_1() -> str:
-    """Returns the `world` substring."""
-    return "world"
-
-
-@step
-def step_2(input_one: str, input_two: str) -> None:
-    """Combines the two strings at its input and prints them."""
-    combined_str = input_one + ' ' + input_two
-    print(combined_str)
-
-
-@pipeline
-def my_pipeline():
-    output_step_one = step_1()
-    step_2(input_one="hello", input_two=output_step_one)
-
-
-if __name__ == "__main__":
-    my_pipeline()
-```
-
-```bash
-python run.py
-```
-
-## 👭 4. Start the Dashboard
-
-Open up the ZenML dashboard using this command.
-
-```bash
-zenml show
-```
+1. [E2E Batch Inference](examples/e2e/): Feature engineering, training, and inference pipelines for tabular machine learning.
+2. [Basic NLP with BERT](examples/e2e_nlp/): Feature engineering, training, and inference focused on NLP.
+3. [LLM RAG Pipeline with Langchain and OpenAI](https://github.com/zenml-io/zenml-projects/tree/main/llm-agents): Using Langchain to create a simple RAG pipeline.
+4. [Huggingface Model to Sagemaker Endpoint](https://github.com/zenml-io/zenml-projects/tree/main/huggingface-sagemaker): Automated MLOps on Amazon Sagemaker and HuggingFace.
 
 # 🗺 Roadmap
 
 ZenML is being built in public. The [roadmap](https://zenml.io/roadmap) is a
 regularly updated source of truth for the ZenML community to understand where
 the product is going in the short, medium, and long term.
 
-ZenML is managed by a [core team](https://zenml.io/company#CompanyTeam) of
+ZenML is managed by a [core team](https://zenml.io/company) of
 developers that are responsible for making key decisions and incorporating
 feedback from the community. The team oversees feedback via various channels,
 and you can directly influence the roadmap as follows:
 
 - Vote on your most wanted feature on our [Discussion
   board](https://zenml.io/discussion).
-- Start a thread in our [Slack channel](https://zenml.io/slack-invite).
+- Start a thread in our [Slack channel](https://zenml.io/slack).
 - [Create an issue](https://github.com/zenml-io/zenml/issues/new/choose) on our
   GitHub repo.
 
@@ -291,7 +201,7 @@ Guide](CONTRIBUTING.md) for all relevant details.
 The first point of call should
 be [our Slack group](https://zenml.io/slack-invite/).
 Ask your questions about bugs or specific use cases, and someone from
-the [core team](https://zenml.io/company#CompanyTeam) will respond.
+the [core team](https://zenml.io/company) will respond.
 Or, if you
 prefer, [open an issue](https://github.com/zenml-io/zenml/issues/new/choose) on
 our GitHub repo.

docs/book/deploying-zenml/zenml-cloud/cloud-system-architecture.md

@@ -5,7 +5,7 @@ description: Different variations of the ZenML Cloud architecture depending on y
 # System Architecture
 
 {% hint style="info" %}
-If you would like to evaluate ZenML Cloud, please [book a demo](https://zenml.io/book-a-demo) and we can help you decide what scenario suits you best!
+Should you be interested in assessing ZenML Cloud, you can create a [free account](https://cloud.zenml.io), which defaults to a [Scenario 1](#scenario-1-full-saas) deployment. To upgrade to different scenarios, please [reach out to us](mailto:cloud@zenml.io).
 {% endhint %}
 
 Machine learning often involves data that is sensitive and thus data security is a fundamental requirement. The ZenML Cloud is flexible enough to meet your security requirements, from easy installations to completely airgapped deployments.
@@ -16,7 +16,7 @@ The ZenML Cloud consists of the following moving pieces:
 * **Single Sign-On (SSO)**: The ZenML Cloud API is integrated with [Auth0](https://auth0.com/) as an SSO provider to manage user authentication and authorization. Users can log in to the ZenML Cloud dashboard using their social media accounts or their corporate credentials.
 * **Secrets Store**: All secrets and credentials required to access customer infrastructure services are stored in a secure secrets store. The ZenML Cloud API has access to these secrets and uses them to access customer infrastructure services on behalf of the ZenML Cloud. The secrets store can be hosted either by the ZenML Cloud or by the customer.
 * **ML Metadata Store**: This is where all ZenML metadata is stored, including ML metadata such as tracking and versioning information about pipelines and models.
-* **ZenML Cloud Agent**: This service is optionally deployed customer-side, and interacts with customer MLOps stack components on behalf of the remote ZenML Cloud control plane.
+* **ZenML Cloud Agent (Optional)**: This service is optionally deployed customer-side, and interacts with customer MLOps stack components on behalf of the remote ZenML Cloud control plane.
 
 The above five interact with other MLOps stack components, secrets, and data in varying scenarios described below.
 

docs/book/deploying-zenml/zenml-cloud/zenml-cloud.md

@@ -4,28 +4,44 @@ description: Your one-stop MLOps control plane.
 
 # ☁ ZenML Cloud
 
-ZenML Cloud is a Software-as-a-Service (SaaS) platform that extends the capabilities of the open-source ZenML product. It provides you with a centralized control plane to effortlessly launch and manage their ZenML server instances. The core foundation of ZenML Cloud remains the powerful open-source offering, but with ZenML Cloud, you gain access to a host of additional features designed to streamline the machine learning workflow.
+One of the most straightforward paths to start with a deployed ZenML server is through [ZenML Cloud](https://zenml.io/cloud). ZenML Cloud eliminates the need for you to dedicate time and resources to deploy and manage the ZenML server, allowing you to focus primarily on your MLOps workflows.
 
 <div data-full-width="true">
-
-<figure><img src="../../.gitbook/assets/cloud_architecture_scenario_1.png" alt=""><figcaption><p>An architectural overview of the ZenML Cloud</p></figcaption></figure>
-
+<figure><img src="../../.gitbook/assets/zenml-cloud-tenant-overview.png" alt=""><figcaption><p>ZenML Cloud comes equipped with a powerful cloud-only dashboard</p></figcaption></figure>
 </div>
 
+{% hint style="info" %}
+Should you be interested in assessing ZenML Cloud, you can simply create a [free account](https://cloud.zenml.io). Learn more about ZenML Cloud on the [ZenML Website](https://zenml.io/cloud).
+{% endhint %}
+
 ## Key features
 
-ZenML Cloud brings convenience to your machine learning workflows by allowing you to deploy a managed instance of ZenML servers with a single click. This means you can set up and manage your machine learning pipelines effortlessly without the hassle of dealing with infrastructure complexities. We take care of all the necessary upgrades and backups, ensuring that your system remains up-to-date and resilient while you focus on your core MLOps tasks. As a valued cloud customer, you'll also benefit from priority support, ensuring that you receive the assistance you need to make the most of our platform.
+ZenML Cloud is a Software-as-a-Service (SaaS) platform that enhances the functionalities of the open-source ZenML product. It equips you with a centralized interface to seamlessly launch and manage ZenML server instances. While it remains rooted in the robust open-source offering, ZenML Cloud offers added features designed to optimize your machine learning workflow.
 
-Data security and privacy are of utmost importance at ZenML Cloud. You can connect your infrastructure safely and securely and we track only metadata through an encrypted connection, ensuring that your sensitive information remains confidential. Our platform offers seamless integration with your cloud services through service connectors, making it easy to connect and utilize various cloud resources without compromising data security. Additionally, we store your secrets in a safe and secure isolated environment, providing an extra layer of protection for your valuable data.
+### Managed ZenML Server (Multi-tenancy)
 
-As a ZenML Cloud user, you also gain early access to a powerful control plane that centralizes user management and streamlines your workflows. With centralized management of users, handling access and permissions becomes efficient and organized. Moreover, ZenML Cloud users enjoy exclusive access to a range of cloud-only features, providing you with a competitive edge and the opportunity to stay ahead in the rapidly evolving field of machine learning.
+ZenML Cloud 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 Cloud user, you'll also have priority support, giving you the necessary aid to fully utilize the platform.
 
-{% hint style="info" %}
-ZenML Cloud is currently in the beta phase, offering users the opportunity to host a managed ZenML instance and gain early access to the exciting new features mentioned above. Beta users will receive priority access to the enhanced functionalities and dedicated support to ensure a smooth onboarding experience.
-{% endhint %}
+### Maximum data security
+
+At ZenML Cloud, 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 Cloud 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](cloud-system-architecture.md#scenario-2-hybrid-saas-with-customer-secret-store-managed-by-zenml).
+
+Click [here](cloud-system-architecture.md) to understand about the ZenML Cloud system architecture.
+
+### Role-based access control and permissions
+
+Utilizing ZenML Cloud provides you with early 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.
+
+### A brand-new, modern MLOps experience
+
+![Walkthrough of ZenML Model Control Plane](../../.gitbook/assets/mcp_walkthrough.gif)
+
+We have built the ZenML Cloud from the ground-up. With ZenML Cloud, 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).
+
+### Triggers, CI/CD, Reports and more
 
-## Coming soon...
+Additionally, ZenML Cloud users get exclusive access to an array of cloud-specific features, such as triggering pipelines directly through the dashboard, integrating with your code repository CI/CD system, generating usage reports and more.
 
-<table data-card-size="large" data-view="cards" data-full-width="true"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><mark style="color:purple;"><strong>CI/CD/CT with Triggers</strong></mark></td><td>Continuous Integration, Continuous Deployment, and Continuous Training are all integrated into your repository.</td><td><a href="https://zenml.io/cloud-features/continuous-integration-and-delivery-ci-cd">https://zenml.io/cloud-features/continuous-integration-and-delivery-ci-cd</a></td></tr><tr><td><mark style="color:purple;"><strong>ML Model Control Plane</strong></mark></td><td>All your models are conveniently gathered in one place. Up-to-date information about your training, deployments, and endpoints all within one view.</td><td><a href="https://zenml.io/cloud-features/ml-models-control-plane">https://zenml.io/cloud-features/ml-models-control-plane</a></td></tr><tr><td><mark style="color:purple;"><strong>Managed MLOps infrastructure</strong></mark></td><td>ZenML Cloud also includes hosted instances of support stack components like MLflow.</td><td><a href="https://zenml.io/cloud-features/managed-ml-ops-infrastructure">https://zenml.io/cloud-features/managed-ml-ops-infrastructure</a></td></tr><tr><td><mark style="color:purple;"><strong>Tenants and Workspaces</strong></mark></td><td>Create multiple ZenML tenants and workspaces within tenants to subdivide your team's efforts neatly.</td><td><a href="https://zenml.io/cloud-features/tenants-and-workspaces">https://zenml.io/cloud-features/tenants-and-workspaces</a></td></tr><tr><td><mark style="color:purple;"><strong>User Management</strong></mark></td><td>User permission management and project-specific configurations</td><td><a href="https://zenml.io/cloud-features/user-management">https://zenml.io/cloud-features/user-management</a></td></tr><tr><td><mark style="color:purple;"><strong>Role Based Access Control</strong></mark></td><td>Manage permissions and roles at an organizational level</td><td><a href="https://zenml.io/cloud-features/role-based-access-control">https://zenml.io/cloud-features/role-based-access-control</a></td></tr></tbody></table>
+Learn more about ZenML Cloud on the [ZenML Website](https://zenml.io/cloud).
 
-<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
+<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>