UbiOps

UbiOps – AI Model Serving & Orchestration Documentation

UbiOps provides a unified platform to deploy, manage, and orchestrate AI workloads across on-premises, hybrid cloud, and multi-cloud environments. It enables you to run models locally or at scale, with built-in MLOps features to control costs, ensure compliance, and prevent vendor lock-in. Designed for AI teams, IT, and leadership, UbiOps accelerates from pilot to production, supporting a range of AI use cases from LLMs to computer vision and traditional data science models.

How UbiOps Works

1. Single interface for all infrastructure: Run AI workloads locally, in a hybrid cloud, or across multiple clouds from one management console.
2. Orchestration across environments: Deploy and manage models across Kubernetes, virtual machines, and bare metal, with seamless resource sourcing from different clusters.
3. Cost and governance controls: Stay in control of compute costs, comply with AI regulations, and prevent vendor lock-in.
4. End-to-end MLOps: Built-in API management, version control, scaling algorithms, monitoring, auditing, resource prioritization, security, and access management.
5. Production-ready AI: From experimentation to production, supporting a wide range of AI workloads including Generative AI and Computer Vision.

Use Cases and Capabilities

• On-demand GPUs and scalable AI workloads
• Seamless deployment of LLMs, computer vision, and data science models
• Centralized management to prevent shadow IT and promote reproducibility
• Integration-friendly architecture for private AI on any infrastructure
• Automation and orchestration across multi-cloud environments

How to Use UbiOps

1. Connect your infrastructure (Kubernetes, VMs, Bare Metal) and set up your resource pools.
2. Upload or connect AI models (LLMs, CV models, time-series models, etc.).
3. Define workflows and APIs for serving, orchestration, and monitoring.
4. Monitor, scale, and govern across environments from a single pane of glass.

Core Features

• Single interface to run AI workloads locally, in hybrid cloud, or across multiple clouds
• End-to-end MLOps: API management, version control, scaling algorithms, monitoring, auditing, security, and access management
• Orchestration across Kubernetes, Virtual Machines, and Bare Metal
• On-demand GPU support for scalable AI inference
• Privacy-conscious deployment with control over data and compute costs
• Prevention of vendor lock-in and shadow IT
• Production-ready deployment for Generative AI, Computer Vision, Time Series, and more
• Intuitive UI for AI teams, IT, and leadership with governance and cost controls