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FastScore™ Deploy

Deploy allows users to interact with Engine natively using the FastScore SDK through their lab environment.

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FastScore™ Deploy

As the organization adopts FastScore as the standard way to deploy machine learning models in production, data science teams need easy access to test and iterate their work in Engine. Deploy allows users to interact with Engine natively using the FastScore SDK through their lab environment, like Jupyter Notebooks, to test the execution of machine learning models in a production like environment.

FastScore™ Deploy Benefits
  • Connect your existing model creation environment to FastScore
  • Test and update the execution of your model quickly without changing tools
  • Replicate production systems in development, ensuring each model can be deployed before it leaves the data science team
Deploy

Our Customers

 
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FastScore™ Deploy Features

 

 

 

FastScore SDK allows users to interact with Manage and Engine through their creation environment (like Jupyter notebooks)

 

 

 

 

 

Store model code directly through Manage, allowing desired downstream teams access outside of the model lab environment

 

 

 

 

 

Quickly test and update code to ensure the model executes within Engine