Kafeido-mlops

Enterprise-grade MLOps platform that organizes GPU resources and AI models efficiently, with seamless deployment options for both on-premise and cloud environments.

kafeido-mlops platform

Key Features

GPU Resource Management

Efficiently organize and allocate GPU resources across your AI/ML workloads for optimal performance.

Hybrid Deployment

Deploy seamlessly on-premise or in the cloud, supporting flexible infrastructure strategies.

OpenShift & Kubeflow

Full support for OCP backed by Red Hat and comprehensive Kubeflow API integration.

Model Organization

Centralized management and version control for all your AI models in one platform.

Cost Optimization

Reduce AI/ML infrastructure costs while maintaining high performance and scalability.

Industrial AI Transition

Accelerate your organization's move to AI generation with enterprise-ready tools.

Technical Specifications

  • OpenShift Container Platform (OCP) support
  • Comprehensive Kubeflow API integration
  • Multi-GPU cluster management
  • Automated model deployment pipelines
  • Resource allocation and scheduling
  • Real-time monitoring and analytics
  • Enterprise security and compliance
  • Containerized deployment architecture
  • REST API for custom integrations
  • High availability and fault tolerance
MLOps Architecture

Use Cases

Enterprise AI Labs

Manage multiple AI projects and teams with centralized GPU resource allocation and model management.

Research Institutions

Accelerate AI research with efficient resource sharing and experiment tracking capabilities.

Manufacturing AI

Deploy AI models for quality control, predictive maintenance, and process optimization.

Transform Your MLOps Infrastructure

Join leading enterprises that trust kafeido-mlops for their AI/ML operations

Schedule a Demo

Footprint-AI

Empowering enterprises with sustainable AI/ML infrastructure solutions. Our kafeido.app platform helps organizations optimize GPU resources, reduce costs, and accelerate their AI transformation journey.