May 15, 2017

Maximize flexibility to design models and ensure they are ready for deployment.

We are excited to make placing models into FastScore simple.  Watch a step-by-step demo of our new Jupyter integration with Matthew Mahowald, Product Manager/Data Scientist.

A simple restful API with Jupyter allows you to verify how models are behaving before they are uploaded into FastScore engines.  Prepare and upload models, validate data schemas, identify potential production failures and errors, leverage your full data science stack, including libraries like Pandas and data.table, as well as validate, score and gain feedback.  Watch and get answers to our most frequently asked questions, and more.

  • What languages does the Jupyter platform support for FastScore?
  • Can I check and validate my models before uploading them to FastScore engines?
  • How can I ensure my model deploys before I hand it to the production team?

Jupyter integration allows the data science team maximum flexibility to design their models in familiar environments while simultaneously ensuring they are ready for deployment.   With Jupyter and FastScore you can test locally, and deploy globally.



Tagged: data science, FastScore, Open Data Group, Analytic Deployment, Jupyter