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 +1,45 @@
 # 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 project designed to enhance collaboration among data scientists, application developers, and SREs working on integrating or managing self-hosted AI/ML models.
+
+### Core Components of KitOps
+
+**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 that is compatible with nearly any tool.
 
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+**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 or governance.
 
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+**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, run, and deploy ModelKits using Kitfiles. Whether you are packaging a new model for development or deploying an existing model into production, the Kit CLI provides the necessary commands and functionalities to streamline your workflow.
 
-## Why use KitOps?
+### The Goal of KitOps
 
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+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 drives greater speed, security, and collaboration for teams hosting their own AI/ML models.
 
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+_For application developers_ 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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+_For data scientists_ KitOps enables them to innovate in AI/ML without the usual infrastructure distractions. It simplifies dataset and model managementand sharing, fostering closer collaboration with developers. With KitOps, data scientists can spend more time experimenting and less time grappling with traditional software development tools.
+
+Join us in shaping the future of AI/ML collaboration with KitOps.
 
 ## 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 solves the reproducibility issues that frequently arise in AI/ML development, ensuring consistency and reliability across project stages.
+
+### Deployment Ready ###
+Designed with a focus on deployment, ModelKits package assets in standard formats so they're compatible with nearly any tool - helping you get your model to production 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.
 
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+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.
 
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+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.