UbiOps provides a unified MLOps platform that enables the deployment and management of AI workloads across local, hybrid, and multi-cloud environments. By streamlining AI operations with built-in features like version control and automatic resource scaling, UbiOps reduces infrastructure overhead and development costs by up to 80%.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
RIFounders
Product
Problem
Organizations face challenges in deploying and managing AI workloads across diverse infrastructure environments, including local servers, hybrid setups, and multi-cloud deployments. Building and maintaining the necessary AI infrastructure can be costly and time-consuming, diverting resources from core model development. Furthermore, vendor lock-in and shadow IT can create complexities in managing AI workloads and ensuring compliance with data processing regulations.
Solution
UbiOps provides a unified platform designed to streamline AI operations, enabling the deployment and management of AI workloads across various infrastructure environments. The platform offers built-in MLOps features such as version control, environment management, monitoring, auditing, security, team collaboration, and governance. UbiOps simplifies the AI production process, allowing users to deploy AI models, including pre-trained LLMs, computer vision models, and traditional data science models, as scalable inference endpoints. By automating resource scaling and providing a centralized interface, UbiOps reduces infrastructure overhead, minimizes computing costs, and prevents vendor lock-in.
Target Audience
UbiOps is designed for AI teams, AI leaders, and IT teams seeking to streamline AI operations, reduce infrastructure costs, and maintain control over AI workloads across diverse environments.
Features
- Unified interface for deploying AI workloads across local, hybrid, and multi-cloud environments
- Built-in MLOps features including version control, environment management, monitoring, and auditing
- Automatic scaling of resources to optimize GPU utilization and minimize costs
- Support for various AI models, including Generative AI, Computer Vision, and Time Series models
- Orchestration capabilities across Kubernetes, Virtual Machines, and Bare Metal
- API management, security features, and access management
- Integration with NVIDIA AI Enterprise