UbiOps

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Deployed by 5 companies

Run your data science code live in the cloud

Page last modified
April 11 2024

UbiOps is an easy-to-use deployment and serving layer for your data science code. Run your Python & R models and scripts live and use them from anywhere at any time. UbiOps is currently used as a backend to optimize heat networks by Gradyent. Furthermore BAM energy systems uses UbiOps to predict energy usage of buildings and builds their services on top of that.

Pros & Limitations
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Up to 80% reduced time to market
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No in-depth IT knowledge required
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Support for complex pipelines
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Automatic API endpoint creation
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Your code is automatically containerised and deployed on Kubernetes
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Available as SaaS or on-prem version
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As you don't have to install and maintain kubernetes clusters, you give away some control of how this is done. You do have the option to set minimum and maximum amount of active instances
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We support Python and R, but not any other languages
Specification
Specification Title Specification Description
Compatibility
UbiOps serves and hosts any code (Python and R) as a micro-service with an API endpoint.
Automation
Every piece of code that you upload is automatically deployed to a kubernetes pod, making it scalable depending on your specifications.
Modular Design
Every piece of code is containerised and any dependencies are installed in a container too.
Monitoring
Using the integrations with various monitoring tools, you can measure accuracy, F1 scores, data drift and explain local predictions.
Training
Using the integrations with ML services, Sagemaker and other training tools, you can easily push your model from the training environment to production with Ubiops.
Business Efficiency
UbiOps speeds up the time to market of your data science projects.

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Technology Readiness Level

The Technology Readiness Level (TRL) indicates the maturity level of novel technologies. Learn more about the TRL scale used by us.

[9/9]

Development Technology demonstration Mature / Proven
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UbiOps is an easy-to-use deployment and serving layer for your data science code. Run your Python & R models and scripts live and use them from anywhere at any time.

Relative Business Impact

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