Accelerate AI/ML Adoption

Accelerate AI Adoption with a
GPU PaaS and MLOps Tooling

Easily manage the underlying AI infrastructure and AI/ML
tooling your data scientists need to innovate faster, with guardrails included

Is your adoption of AI fast enough?

Rafay empowers companies to accelerate AI adoption by providing a robust platform for seamless deployment, management, and scaling of AI/ML workloads across public cloud and on-premise environments. By making GPU-based infrastructure cheaper and easier to consume, along with providing AI/ML tooling for data scientists, Rafay enables organizations to speed up their AI adoption, driving innovation and a clear competitive edge.

Launch a customizable GPU PaaS in days

Accelerate your time-to-market with high-value NVIDIA hardware by rapidly launching a PaaS for GPU consumption, complete with a customizable storefront experience for your internal and external customers.

Deliver a SageMaker-like experience anywhere

Transform the way you build, deploy, and scale machine learning with Rafay’s comprehensive MLOps platform that runs in your data center and any public cloud.

Provide self-service AI Workbenches to data scientists

Data scientists can quickly access a fully functional data science environment without the need for local setup or maintenance. They can be more productive, sooner, by focusing on coding and analysis rather than managing AI infrastructure.

Consume a scalable, cost-effective GenAI playground to enable experimentation

Help developers experiment with GenAI by enabling them to rapidly train, tune, and test large models, along with approved tools such as vector databases, inference servers, etc.

Focus on AI development, not on infrastructure

Rafay helps platform teams build an enterprise-class AI practice
for their organizations while realizing the following benefits:

Harness the Power of AI Faster

Complex processes and steep learning curves shouldn’t prevent developers and data scientists from building AI applications. A turnkey MLOps toolset with support for both traditional and GenAI (aka LLM-based) models allows them to be more productive without worrying about infrastructure details

Reduce the
Cost of AI

By utilizing GPU resources more efficiently with capabilities such as GPU matchmaking, virtualization and time-slicing, enterprises reduce the overall infrastructure cost of AI development, testing and serving in production.

Increase Productivity for Data Scientists

Provide data scientists and developers with a unified, consistent interface for all of the MLops and LLMOps work regardless of the underlying infrastructure, simplifying training, development, and operational processes.

Download the White Paper
Scale AI/ML Adoption

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

Download Now

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User Access Reports for Kubernetes

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Access reviews are required and mandated by regulations such as SOX, HIPAA, GLBA, PCI, NYDFS, and SOC-2. Access reviews are critical to help organizations maintain a strong risk management posture and uphold compliance. These reviews are typically conducted on a… Read More

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EC2 vs. Fargate for Amazon EKS: A Cost Comparison

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Kubernetes Management with Amazon EKS

August 20, 2024 / by James Walker

Kubernetes management is the process of administering your Kubernetes clusters, their node fleets, and their workloads. Organizations seeking to use Kubernetes at scale must understand effective management strategies so they can successfully operate containerized applications without sacrificing observability, security, and… Read More

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