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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. 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.
Specification Title | Specification Description |
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Compatibility
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UbiOps serves and hosts any code (Python and R) as a micro-service with an API endpoint.
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Automation
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Every piece of code that you upload is automatically deployed to a kubernetes pod, making it scalable depending on your specifications.
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Modular Design
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Every piece of code is containerised and any dependencies are installed in a container too.
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Monitoring
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Using the integrations with various monitoring tools, you can measure accuracy, F1 scores, data drift and explain local predictions.
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Training
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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.
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Business Efficiency
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UbiOps speeds up the time to market of your data science projects.
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[9/9]
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