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UbiOps

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Deploy, orchestrate, and scale AI workloads across any infrastructure.

Adicionado em:13 de jul. de 2026
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MLOpsAI model deploymentModel servingAI orchestrationGenerative AIComputer vision

Informações da ferramenta

O que é UbiOps?

UbiOps is an AI model serving, deployment, orchestration, and MLOps platform for running scalable AI workloads across local, hybrid-cloud, and multi-cloud infrastructure. It helps AI and IT teams deploy, manage, serve, and scale models as production applications.

Recursos principais

  • AI model deployment and serving

    Deploy AI models as reliable, scalable services and inference APIs across different infrastructure environments.

  • AI workload orchestration

    Orchestrate AI workloads across multiple compute environments and hardware types through a single control plane.

  • MLOps management

    Manage models with cataloging, version management, dependency management, monitoring, auditing, security, team collaboration, and governance capabilities.

  • Workflow and pipeline management

    Build and deploy modular AI applications as workflows in which each object operates as an isolated service that can scale independently.

  • Automatic scaling

    Scale models automatically with scale-to-zero, traffic load balancing, API management, and dynamic scaling across infrastructures.

  • Multi-environment execution

    Run AI workloads across local, hybrid-cloud, and multi-cloud environments, including Kubernetes, virtual machines, and bare metal.

  • On-demand GPU workloads

    Scale AI and machine learning workloads on demand using GPU infrastructure for inference and training.

Casos de uso

  • #1

    Generative AI deployment

    Deploy large language models and other generative AI models as scalable inference APIs.

  • #2

    Computer vision applications

    Run computer vision models as production services with real-time processing and workload scaling.

  • #3

    AI model training

    Offload long-running AI and machine learning training jobs to cloud hardware.

  • #4

    Hybrid and multi-cloud AI operations

    Manage AI workloads across local infrastructure and multiple cloud environments from a single interface.

  • #5

    Centralized AI governance

    Manage AI workloads centrally with monitoring, auditing, security, access management, and resource prioritization.

Melhor para:AI teamsAI leadersIT teamsHealthcare organizationsPublic-sector organizationsCritical-infrastructure organizations

Planos de preços

Pricing is not clearly published by the official source.

Alternativas

Perguntas frequentes

What is UbiOps?
UbiOps is a platform for deploying, serving, managing, orchestrating, and scaling AI and machine learning workloads across local, cloud, hybrid-cloud, and multi-cloud infrastructure.
What types of AI workloads can UbiOps support?
UbiOps supports generative AI, large language model inference, computer vision applications, AI model training, and other AI and machine learning workloads.
Where can UbiOps run AI workloads?
UbiOps can run workloads across local infrastructure, hybrid-cloud and multi-cloud environments, Kubernetes, virtual machines, bare metal, and GPU infrastructure.
Does UbiOps provide automatic scaling?
Yes. Its automatic scaling capabilities include scale-to-zero, traffic load balancing, API management, and dynamic scaling across infrastructures.
Does UbiOps support AI workflows and pipelines?
Yes. UbiOps provides workflow and pipeline management for building and deploying modular AI applications with independently scalable service objects.
What integrations does UbiOps list?
The listed integrations and supported infrastructure include Kubernetes, virtual machines, bare metal, and NVIDIA AI Enterprise.
How much does UbiOps cost?
Pricing is not clearly published by the official source.

UbiOps Contato e mídias sociais

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Informações da ferramenta

Modelo de preçosUnknown
CategoriaFerramentas para Desenvolvedores
Tags
MLOpsAI model deploymentModel servingAI orchestrationGenerative AIComputer vision
IdiomaInglês
Última atualização16 de jul. de 2026