# Boost Productivity with AI Workbenches for Data Scientists & Developers

Provide self-service AI workbenches to developers and data scientists so they can rapidly experiment with, iterate across, and deploy AI models

## Build, train, deploy, and manage AI models fast

## Provide 1-Click AI Dev Environments

Easy configuration and access to Jupyter notebooks with support for pre-configured environments for developing AI models using popular programming languages (e.g., Python, R) and frameworks (e.g., TensorFlow, PyTorch).

## Create a Storefront for AI resources

Data scientists, ML engineers and developers can quickly request and launch GPU powered instances on demand integrated with approval mechanisms. They can select from a curated list of GPU and CPU configurations to suit their specific project’s requirements.

## Train & Serve Models with Ease

Enable users to perform AutoML, hyperparameter tuning, experiments, and more with ease. Deploy and serve models in a serverless manner with embedded support for popular frameworks like Tensorflow, PyTorch etc. Leverage the integrated model registry to accelerate the journey from research to production.

## Enhance Collaboration and Sharing

Multiple users – regardless of location – can work on the same project with shared resources and collaborative tools like shared notebooks and version control. Leverage our integrated 3rd-party application catalog featuring cutting-edge machine learning apps and tools to help data scientists be more productive.

## Providing self-service AI workbenches drives innovation and speeds up time-to-market for AI applications

By providing self-service AI workbenches to developers and data scientists, Rafay customers realize the following benefits:

### Accelerated Innovation

Self-service AI workbenches enable teams to quickly experiment and deploy models, significantly speeding up the innovation cycle

### Enhanced Productivity

Direct access to AI tools empowers data scientists and engineers, reducing dependencies on IT and streamlining workflows

### Reduced Time-to-Market

With faster experimentation and deployment capabilities, companies can bring AI-driven solutions to market more quickly, gaining a competitive edge.

### Optimized Resource Utilization

Self-service AI platforms allow for efficient allocation and scaling of computational resources, ensuring cost-effectiveness and performance optimization.

## 10 Multi-Tenancy Best Practices for Namespaces as a Service (NaaS)

The white paper guides organizations in leveraging Kubernetes effectively for improved resource utilization and cost efficiency.

[DOWNLOAD](https://info@rafay.co/white-papers/multi-tenancy-best-practices-for-namespaces-as-a-service)

## Start a conversation with Rafay

Talk with Rafay experts to assess your infrastructure, explore your use cases, and see how teams like yours operationalize AI/ML and cloud-native initiatives with self-service and governance built in.

[Start a Conversation](https://info@rafay.co/start)