Integrate AI into your Business Processes with GPU-Powered Intelligence

Streamline and accelerate the adoption of GPU infrastructure for AI within enterprises, both generally and in regulated sectors.

A platform controller acts as a secure and compliant on-ramp for enterprises, especially those in regulated industries, to adopt GPU infrastructure for AI. It removes the complexities of managing hybrid GPU resources, automates critical security and compliance measures, and provides a unified way for DevOps and data science teams to leverage the power of GPUs for AI innovation with confidence and speed.

Facilitating GPU Infrastructure Adoption for AI in General Enterprises

  • Simplified Provisioning and Management of Hybrid GPU Resources AI workloads often demand significant computational power, making GPUs essential. The platform controller simplifies the process of provisioning and managing GPU resources, whether they reside on-premise or on public cloud platforms. DevOps engineers can request and access GPU instances through the unified interface without needing to understand the intricacies of different infrastructure environments.

  • Automated Dependency Management for AI Workloads AI applications often have complex dependencies on libraries, frameworks (like TensorFlow or PyTorch), and specific software versions. The platform can automate the management of these dependencies across the hybrid environment, ensuring consistency and reducing compatibility issues that can hinder AI development and deployment on GPU infrastructure.

  • Optimized Resource Allocation and Cost Efficiency GPU instances can be expensive. The platform controller can intelligently manage and allocate GPU resources based on workload demands across the hybrid cloud. This optimization ensures that GPUs are utilized efficiently, minimizing costs and maximizing the return on investment in AI infrastructure. For example, less demanding tasks might run on CPU instances, freeing up GPUs for intensive model training.

  • Faster Experimentation and Iteration for AI Models By simplifying access to GPU resources and managing dependencies, the platform accelerates the development lifecycle for AI models. Data scientists and machine learning engineers can provision the necessary GPU power quickly, experiment with different model architectures and hyperparameters, and iterate faster, leading to quicker innovation in AI-powered services.

  • Consistent Environments for AI Development and Deployment The unified interface helps create consistent environments for AI development, testing, and deployment, regardless of the underlying infrastructure. This consistency reduces the “it works on my machine” problem and streamlines the transition of AI models from research to production on GPU infrastructure.

Accelerating GPU Infrastructure Adoption for AI in Regulated Industries

The benefits above are crucial for regulated industries, with the added layer of automated governance making GPU adoption for AI more viable and secure

  • Automated Security and Compliance for GPU-Enabled AI Workloads Handling sensitive data with AI models on GPU infrastructure requires stringent security and compliance. The platform controller automatically implements obligated security controls (e.g., data encryption, access controls, network segmentation) and compliance policies specific to the industry (e.g., HIPAA, GDPR, FedRAMP) for the GPU resources and the AI workloads running on them, regardless of their location.

  • Auditable and Compliant GPU Resource Provisioning and Usage The platform can maintain detailed audit logs of how GPU resources are provisioned, accessed, and utilized for AI workloads. This auditability is critical for demonstrating compliance to regulatory bodies and simplifies the audit process.

  • Secure Data Handling and Model Deployment on GPUs Regulated industries often deal with sensitive data that needs to be processed and analyzed using AI. The platform ensures that data accessed and processed by AI models running on GPUs adheres to strict data governance policies and that deployed models are secure and compliant.

  • Faster Time-to-Compliance for AI Initiatives By automating security and compliance controls for GPU infrastructure, the platform significantly reduces the time and effort required to meet regulatory requirements for AI projects. This allows regulated enterprises to adopt GPU-powered AI faster without compromising on compliance.

  • Enabling Innovation in AI for Sensitive Applications The secure and compliant foundation provided by the platform can enable regulated industries to explore and implement innovative AI applications (e.g., medical image analysis, fraud detection, risk assessment) that rely on GPU acceleration, even when dealing with highly sensitive data.

  • Reduced Risk and Increased Trust Automating security and compliance for GPU infrastructure reduces the risk of security breaches and non-compliance penalties. This builds trust with customers, regulators, and internal stakeholders, fostering a more supportive environment for adopting AI powered by GPUs.