Accelerate AI Development with Self-Service AI Workbenches
Provide data scientists, ML engineers, and developers with self-service AI workbenches for rapidly developing, training, experimenting with, and deploying AI models.
Build, train, deploy, and manage AI models fast
Launch Ready-to-Use AI Workbenches
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).
Provision consistent AI development environments on demand, reducing setup time and avoiding the configuration overhead associated with building environments manually.
Create a Storefront for Scalable AI resources
Data scientists, ML engineers, and developers can quickly request and launch GPU-powered instances on demand, with integrated approval mechanisms and governance. They can select from a curated catalog of GPU and CPU configurations based on their project requirements. This self-service approach gives users access to the infrastructure they need while helping platform teams maintain control over resource allocation and consumption.
Train & Serve Models with Ease
Enable users to perform AutoML, hyperparameter tuning, experiments, and more with ease. Deploy and serve models with support for popular frameworks such as TensorFlow and PyTorch. Leverage the integrated model registry to help teams track models and accelerate the journey from research to production.
By bringing development environments, compute resources, experimentation, and model management together, AI workbenches provide a more consistent path from experimentation to production.
Enhance Collaboration and Sharing
Multiple users – regardless of location – can work on the same project with shared resources and collaborative tools such as shared notebooks and version control. Leverage the integrated third-party application catalog to provide data scientists and ML engineers with access to machine learning applications and tools. Standardized environments also help teams reduce configuration differences between projects and provide a more consistent foundation for AI development.
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 give teams rapid access to development environments and compute resources, allowing them to experiment and iterate faster.
Enhanced Productivity
Direct access to AI development tools, notebooks, frameworks, and infrastructure reduces dependencies on IT and streamlines development workflows.
Reduced Time-to-Market
Standardized environments and self-service infrastructure can help teams move more efficiently from experimentation and model development toward production.
Optimized Resource Utilization
Self-service access to GPU and CPU resources allows organizations to provision compute based on workload requirements while applying governance and approval controls.
From AI Experimentation to Production
Provision an AI Development Environment
Launch a pre-configured environment with the tools and frameworks required for a specific AI project.
Select the Right Compute
Choose GPU or CPU resources based on the requirements of training, experimentation, or inference workloads.
Develop and Experiment
Use Jupyter notebooks and integrated tools to develop, test, and iterate on AI models.
Train and Manage Models
Run training and experimentation workflows while managing models through an integrated model registry.
Deploy AI Models
Move validated models from development into production environments for serving and inference.
Self-Service Enterprise AI with Governance
Maintain control over infrastructure, resource allocation, and access while giving developers and data scientists the autonomy to provision the environments and compute they need.
Controlled Resource Access
Provide access to approved GPU, CPU, and AI resources through a curated catalog.
Standardized Environments
Give teams consistent development environments and approved tools across projects.
Centralized Management
Maintain visibility and control over AI workloads and infrastructure while enabling self-service development.
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.
Frequently Asked Questions
An AI workbench is a self-service development environment that gives data scientists and developers the tools, compute resources, frameworks, and services they need to build, train, experiment with, and deploy AI and machine learning models.
AI workbenches help teams reduce infrastructure setup time, standardize development environments, access compute on demand, and move more efficiently from experimentation to production.
An AI workbench can include Jupyter notebooks, pre-configured development environments, GPU and CPU compute, machine learning frameworks, experimentation tools, model management capabilities, and resources for deploying AI models.
Rafay provides self-service AI development environments with access to configurable compute resources, integrated tools, and workflows for developing, training, and deploying AI models, while giving platform teams governance and control over the underlying infrastructure.
