> For the complete documentation index, see [llms.txt](https://rocketml.gitbook.io/rocketml-user-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://rocketml.gitbook.io/rocketml-user-guide/master.md).

# What is RocketML

RocketML is machine learning platform for distributed computing on the cloud. Data scientists can effortlessly build and train machine learning (ML) models using CPU/GPU clusters and deploy ML models as Docker containers with a REST API endpoint.

The RocketML stack consists of four layers:

1. **Cloud Orchestrator**: interacts with underlying cloud infrastructure to coordinate different cloud resources and manage end-to-end machine learning application life-cycle. These include Virtual Machines (VMs), Storage, Databases, IAM policies, Docker and Kubernetes resources.
2. **High Performance Computing Backend**: allows interactive creation and management of multi-node CPU and GPU clusters where high throughput and tightly coupled workloads can be executed in parallel.
3. **MLOps Framework**: facilitates several user tasks. For instance, it (i) creates and organizes user experiments (code, data, configuration and results); (ii) allows data science code to be packaged in a format reproducible on different platforms; (iii) deploys machine learning models in diverse serving environments, as well as stores, annotates, discovers, and manages models in a central repository.
4. **Research Workbench**: provides development tools such as JupyterLab, VSCode, and RStudio for coding in Python and R. The workbench comes pre-installed with several open-source libraries for a wide range of workloads.

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