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docs/overview.md

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+---
+id: overview
+title: What is ClearML?
+slug: /
+---
+
+# ClearML Documentation
+
+## Overview
+Welcome to the documentation for ClearML, the end to end platform for streamlining AI development and deployment. ClearML consists of three essential layers:
+1. Cloud/On-Prem Agnostic [**Infrastructure Control Plane**](#infrastructure-control-plane)
+2. [**AI Development Center**](#ai-development-center)
+3. [**GenAI App Engine**](#genai-app-engine)
+
+Each layer provides distinct functionality to ensure an efficient and scalable AI workflow from development to deployment.
+
+![Webapp gif](img/gif/webapp_screenshots.gif)
+
+---
+
+## Infrastructure Control Plane
+The Infrastructure Control Plane serves as the foundation of the ClearML platform, offering compute resource provisioning and management, enabling administrators to make the compute available through GPUaaS capabilities and no-hassle configuration.  
+Utilizing the Infrastructure Control Plane, DevOps and IT teams can manage and optimize GPU resources to ensure high performance and cost efficiency.
+
+### Features
+- **Resource Management:** Automates the allocation and management of GPU resources.
+- **Workload Autoscaling:** Seamlessly scale GPU resources based on workload demands.
+- **Monitoring and Logging:** Provides comprehensive monitoring and logging for GPU utilization and performance.
+- **Cost Optimization:** Consolidate cloud and on-prem compute into a seamless GPUaaS offering 
+- **Deployment Flexibility:** Easily run your workloads on both cloud and on-premises compute.
+
+---
+
+## AI Development Center
+The AI Development Center offers a robust environment for developing, training, and testing AI models. It is designed to be cloud and on-premises agnostic, providing flexibility in deployment.
+
+### Features
+- **Integrated Development Environment:** A comprehensive IDE for training, testing, and debugging AI models.
+- **Model Training:** Scalable and distributed model training and hyperparameter optimization.
+- **Data Management:** Tools for data preprocessing, management, and versioning.
+- **Experiment Tracking:** Track metrics, artifacts and log. manage versions, and compare results.
+- **Workflow Automation:** Build pipelines to formalize your workflow
+
+---
+
+## GenAI App Engine
+The GenAI App Engine is designed to deploy large language models (LLM) into GPU clusters and manage various AI workloads, including Retrieval-Augmented Generation (RAG) tasks. This layer also handles networking, authentication, and role-based access control (RBAC) for deployed services.
+
+### Features
+- **LLM Deployment:** Seamlessly deploy LLMs into GPU clusters.
+- **RAG Workloads:** Efficiently manage and execute RAG workloads.
+- **Networking and Authentication:** Deploy GenAI through secure, authenticated network endpoints
+- **RBAC:** Implement RBAC to control access to deployed services.
+
+---
+
+## Getting Started
+To begin using the ClearML, follow these steps:
+1. **Set Up Infrastructure Control Plane:** Allocate and manage your GPU resources.
+2. **Develop AI Models:** Use the AI Development Center to develop and train your models.
+3. **Deploy AI Models:** Deploy your models using the GenAI App Engine.
+
+For detailed instructions on each step, refer to the respective sections in this documentation.
+
+---
+
+## Support
+For feature requests or bug reports, see ClearML on [GitHub](https://github.com/clearml/clearml/issues).
+
+If you have any questions, join the discussion on the **ClearML** [Slack channel](https://joinslack.clear.ml), or tag your questions on [stackoverflow](https://stackoverflow.com/questions/tagged/clearml) with the **clearml** tag.
+
+Lastly, you can always find us at [support@clearml.ai](mailto:support@clearml.ai?subject=ClearML).