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.
SageMaker-like service for private clouds
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 any public cloud or data center.
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Turnkey MLOps platform for all of your developers and data scientists – with guardrails included.
MLOps made easy for public cloud & data centers
Let your data scientists leverage the power of Kubeflow, Ray and MLflow without the hassle of managing the underlying infrastructure and the software in public clouds and in your private data center. Eliminate the operational complexity associated with infrastructure and software lifecycle management.
Provide a consistent MLOps experience for data scientists
Provide data scientists and developers with a unified, consistent interface regardless of the underlying infrastructure, simplifying training, development, and operational processes.
Deliver end-to-end machine learning pipelines
Streamline your ML workflows with seamless integration from data ingestion to model deployment and monitoring, all within a single, cohesive solution.
Customize MLOps to your preferred AI environments
Allow ML environment customization to suit specific requirements, including support for different machine learning platforms (Kubeflow, MLflow and Ray), frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
Centralized control for Platform Teams
Platform teams deliver much-needed capabilities to data scientists as a service, while having the ability to manage, monitor, and secure environments according to their organization’s policies. This includes control over updates, patches, and system configurations.
Accelerate enterprise AI/ML initiatives with confidence.
Organizations use Rafay to operate their machine learning workloads wherever it makes the most sense (for cost, performance or compliance reasons) while realizing the following benefits:
Accelerated ML Development
Empower teams to quickly build, train, and deploy machine learning models, significantly reducing time-to-market. Integrated AI tools let data scientists and developers focus on innovation and deliver impactful results faster.
No Vendor Lock-In
Operating in public clouds or on premises allows businesses to avoid being tied to a single cloud vendor's ecosystem, providing flexibility to switch tools or platforms as needed.
Reduced Costs
Implementing a standardized set of ML workflows and tools eliminates resource wastage, puts an end to the use of expensive, manual processes, and significantly reduces the risk of cloud sticker shock resulting from cloud AI tools adoption.
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Try the Rafay Platform for Free
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!
