GPU/Neocloud Billing using Rafay’s Usage Metering APIs
Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption.
SERVICES YOU CAN LAUNCH WITH THE RAFAY PLATFORM
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.
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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.
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See for yourself how to turn static compute into self-service engines. Deploy AI and cloud-native applications faster, reduce security & operational risk, and control the total cost of Kubernetes operations by trying the Rafay Platform!
