Self-Service AI Workbenches

Boost Productivity with AI Workbenches for Data Scientists

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

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Designed to empower data scientists, analysts, and even non-technical users to 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.

Download the White Paper
Scale AI/ML Adoption

Delve into best practices for successfully leveraging Kubernetes and cloud operations to accelerate AI/ML projects.

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Most Recent Blogs

Image for Democratizing GPU Access: How PaaS Self-Service Workflows Transform AI Development

Democratizing GPU Access: How PaaS Self-Service Workflows Transform AI Development

April 11, 2025 / by Gautam Chintapenta

A surprising pattern is emerging in enterprises today: End-users building AI applications have to wait months before they are granted access to multi-million dollar GPU infrastructure.  The problem is not a new one. IT processes in… Read More

Image for Rafay and Netris: Partnering to speed up consumption and monetization for GPU Clouds

Rafay and Netris: Partnering to speed up consumption and monetization for GPU Clouds

March 12, 2025 / by Haseeb Budhani

Rafay, a pioneer in delivering platform-as-a-service (PaaS) capabilities for self-service compute consumption, and Netris, a leader in networking Automation, Abstraction, and Multi-tenancy for AI & Cloud operators , are collaborating to help GPU Cloud Providers speed up consumption… Read More

Image for Is Fine-Tuning or Prompt Engineering the Right Approach for AI?

Is Fine-Tuning or Prompt Engineering the Right Approach for AI?

March 6, 2025 / by Rajat Tiwari

While prompt engineering is a quick and cost-effective solution for general tasks, fine-tuning enables superior AI performance on proprietary data. We previously discussed how building a RAG-based chatbot for enterprise data paved the way for creating a… Read More

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