Update the kitops overview.

docs/.vitepress/config.mts

@@ -57,10 +57,9 @@ export default defineConfig({
         ]
       },
       {
-        text: 'CLI',
+        text: 'ModelKit',
         items: [
-          { text: 'Download & Install', link: '/docs/cli/installation' },
-          { text: 'Command Reference', link: '/docs/cli/kit' },
+          { text: 'Introduction', link: '/docs/modelkit/intro' },
         ]
       },
       {
@@ -71,6 +70,13 @@ export default defineConfig({
           { text: 'Benefits', link: '/docs/kitfile/benefits' },
         ]
       },
+      {
+        text: 'CLI',
+        items: [
+          { text: 'Download & Install', link: '/docs/cli/installation' },
+          { text: 'Command Reference', link: '/docs/cli/kit' },
+        ]
+      },
       {
         text: 'MLOps with Kitfile',
         items: [

docs/src/docs/overview.md

@@ -1,29 +1,56 @@
 # Getting started
 
-:::info
-Quick TL; DR about what is KitOps and why it is awesome
-:::
-
 ## What is KitOps?
 
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+KitOps is an innovative open-source initiative designed to enhance collaboration among data scientists, application developers, and operators in the field of artificial intelligence (AI) and machine learning (ML).
 
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+### Core Components of KitOps
 
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+**ModelKit:** At the heart of KitOps is the ModelKit, an OCI-compliant packaging format that enables the seamless sharing of all necessary artifacts involved in the AI/ML model lifecycle. This includes datasets, code, configurations, and the models themselves. By standardizing the way these components are packaged, ModelKit facilitates a more streamlined and collaborative development process.
 
-## Why use KitOps?
+**Kitfile:** Complementing the ModelKit is the Kitfile, a YAML-based configuration file that simplifies the sharing of model, dataset, and code configurations. The Kitfile is designed with both ease of use and security in mind, ensuring that configurations can be efficiently packaged and shared without compromising on safety.
+
+**Kit CLI:** Bringing everything together is the Kit Command Line Interface (CLI). The Kit CLI is a powerful tool that enables users to create, manage, and deploy ModelKits using Kitfiles. Whether you are packaging a new model for distribution or deploying an existing model into production, the Kit CLI provides the necessary commands and functionalities to streamline your workflow.
+
+### The Goal of KitOps
+
+The primary goal of KitOps is to bridge the gaps between data science, software development, and operational deployment. By providing tools that standardize and simplify the sharing of AI/ML artifacts, KitOps aims to foster a more collaborative and efficient environment for innovation in the AI/ML space.
 
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+Whether you are a data scientist looking to share your latest model, an application developer integrating AI/ML into software, or an operator deploying models at scale, KitOps offers the tools and frameworks to support your work.
 
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+Join us in shaping the future of AI/ML collaboration with KitOps.
 
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 ## Benefits of KitOps
 
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+KitOps is not just another tool; it's a comprehensive CLI and packaging system specifically designed for the AI/ML workflow. It acknowledges the nuanced needs of AI/ML projects, such as:
+
+### Management of Unstructured Datasets ###
+AI/ML projects often deal with large, unstructured datasets, such as images, videos, and audio files. KitOps simplifies the versioning and sharing of these datasets, making them as manageable as traditional code.
+
+### Synchronised Data and Code Versioning ### 
+One of the core strengths of KitOps is its ability to keep data and code versions in sync. This crucial feature tackles the reproducibility issues that frequently arise in AI/ML development, ensuring consistency and reliability across project stages.
+
+### Enhanced Collaboration ### 
+KitOps introduces ModelKits, a novel way to package AI/ML projects that streamlines teamwork within and across departments. This ensures that all stakeholders are aligned and working with the correct versions of data and models, fostering a unified development environment.
+
+### Deployment Ready ###
+Designed with deployment in focus, ModelKits alleviate common deployment challenges, bridging the gap between development and production smoothly. This readies your project for the market faster and more efficiently.
+
+### Standards-Based Approach ###
+KitOps champions openness and interoperability through its core components, ensuring seamless integration into your existing workflows:
+
+ModelKits are designed as OCI (Open Container Initiative) artifacts, making them fully compatible with the Docker image registries and other OCI-compliant storage solutions you already use. This compatibility allows for an easy and familiar integration process. By adhering to widely accepted standards, KitOps ensures you're not tied to a single vendor or platform. This flexibility gives you the freedom to choose the best tools and services for your needs without being restricted by proprietary formats.
+
+
+Kitfiles leverage the simplicity and ubiquity of YAML for configuration, offering an accessible and straightforward way to specify the details of your AI/ML projects.
+
+The Kit CLI is an open-source tool, developed and supported by a community passionate about advancing AI/ML collaboration. Its open-source nature not only fosters innovation and continuous improvement but also allows you to customize and extend its capabilities to meet your unique project requirements.
+
+
+## Why use KitOps?
+
+KitOps clears the path for application developers to harness AI/ML without getting entangled in its complexities. It lets developers concentrate on crafting exceptional applications, while KitOps handles the intricate AI/ML workflows. Whether integrating a new ML model or collaborating on novel features, KitOps accelerates the journey from idea to deployment.
 
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+KitOps is a boon for data scientists, enabling them to explore new frontiers in AI/ML without the usual technical distractions. It simplifies dataset and model management and fosters closer collaboration with developers. With KitOps, data scientists can spend more time innovating and less time navigating the challenges posed by traditional software development tools.
 
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