Managed ML Infrastructure—Not a Black Box Platform
We provide the compute, runtime, and operational foundation required to run machine learning workloading reliably in production—without forcing you into proprietary tools.
GPU & CPU Compute
Flexible Infrastructure
Support for NVIDIA H100, A100, and custom compute resources
Auto Scaling
Automatic scaling for training and inference workloads
Framework Support
PyTorch, TensorFlow, ONNX, JAX, and custom environments
Managed Runtimes
Pre-configured Environments
Optimized runtimes for training and inference workflows
Dependency Management
Automatic handling of libraries and custom dependencies
Version Control
Track and manage runtime versions for reproducibility
Secure & Compliant
Isolated Environments
Dedicated compute with network isolation and encryption
Compliance Ready
HIPAA, SOC 2, and enterprise security standards
Monitoring & Audit
Complete visibility and audit trails for all operations
Supporting Your ML Journey
From Experimentation to Production
Ideate
Define business problems, explore
available data, and formulate actionable ML use cases to drive innovation.
Build
Train
Deploy
Designed to support your entire ML lifecycle, from ideation to enterprise deployment.
Supported Workloads
Provision in Minutes
Cost-efficient for experimentation and development workloads
- Fast spin-up and teardown
- Pay-as-you-go pricing
- Ideal for experimentation
Dedicated Infrastructure
- Predictable latency & throughput
- Isolated compute resources
- Enterprise-grade SLAs
AI at Global Scale
Infrastructure That Doesn’t Get in the Way
Use of favorite tools out-the-box (monitoring of Res CX as the frees)