Operationalizing AI Fabrics with Aviz ONES, NVIDIA Spectrum-X, and Rafay
Discover the new AI operations model available to enterprises that enables self-service consumption and cloud-native orchestration for developers.
SERVICES YOU CAN LAUNCH WITH THE RAFAY PLATFORM
Jupyter Notebooks as a Service: Build a Governed Jupyter Notebook Cloud
Rafay-powered Jupyter Notebooks as a Service (JNBaaS) enables providers and enterprises to offer governed, on-demand JupyterLab environments for data science, AI, and ML teams. Instead of provisioning GPU-backed infrastructure and managing notebook dependencies manually, users can access ready-to-use environments through a self-service experience.
Rafay enables providers and enterprises to deliver hosted JupyterLab environments with GPU access, persistent storage, and pre-configured AI/ML frameworks, while platform teams maintain control over infrastructure, governance, and resource allocation.
Service providers, enterprises, and sovereign cloud operators can deliver GPU-enabled notebook workspaces that give teams faster access to the compute and tools they need for experimentation, model development, and inference.
- Instant Access: Launch JupyterLab environments on demand without manual setup
- Seamless GPU Integration: Access GPU resources for model training, testing, and inference
- AI/ML-Ready Environments: Use pre-configured frameworks, libraries, and persistent storage for data preparation and experimentation
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Simplify and Automate AI/ML Experimentation
Rafay automates how teams access, share, and manage GPU-powered notebook environments—removing operational friction from model development.
Pre-Configured Environments
PyTorch, TensorFlow, CUDA, and other frameworks are ready to use out of the box.
Persistent Storage
Maintain consistent access to datasets and results for reproducible workflows.
Collaboration Controls
Enable secure notebook sharing with role-based access and team visibility.
Integrated Governance
Ensure compliance, traceability, and consistent policies across all environments.
Accelerate Experimentation and Improve Resource Utilization
Move from concept to GPU-backed experimentation in seconds, accelerating AI/ML projects.
Monetize notebooks as value-added SKUs to increase utilization per GPU.
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Enable secure, multi-user access with reproducibility and version control.
Deliver compliant, notebook services aligned with data residency and governance requirements
Build a Jupyter Notebook Cloud
Give data science and AI/ML teams on-demand access to hosted JupyterLab environments backed by dedicated Jupyter Notebook servers, compute, GPUs, storage, and the tools they need to experiment and build models.
Rafay simplifies the infrastructure behind the service.
Automate Provisioning
Deploy standardized, GPU-enabled notebook environments on demand.
Control Resources
Manage compute, storage, and GPU allocation across users and workloads.
Enforce Governance
Apply consistent policies and controls across notebook environments at scale.
Deliver Hosted Jupyter Notebooks
Experience a seamless ready-to-use JupyterLab environment without having to install dependencies, configure GPUs, manage infrastructure, or maintain the underlying environment.
Ready-to-Use Environments
Launch pre-configured JupyterLab environments with the frameworks, libraries, and resources needed for AI/ML development.
Self-Service Access
Give data scientists and ML engineers on-demand access to notebook environments without manual provisioning.
Consistent Workspaces
Provide standardized environments across teams to simplify collaboration and support reproducible workflows.
Secure, Managed Experience
Give users the flexibility to experiment while platform teams maintain control over access, resources, and policies.
Featured Resources
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“We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay.”
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Hybrid Cloud Meets Kubernetes
Learn how to Streamline Kubernetes Ops in Hybrid Clouds with AWS & Rafay
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.
