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Working with Data Scientists for Deployment Success

 
 

 

In This White Paper

 

When it comes to production issues, 50% of data scientists face barriers with pipeline development issues.  This white paper gives you the tools needed to tackle some of your common data science concerns.  Improve operations and ensure your analytic execution environment is the same across the creation, validation, and production stages, by:

  • Learning the importance of enforcing a data schema system on your models; access, discover context, extract, and secure data
  • Understanding the benefits and challenges of data cleaning, data munging, exploratory data analysis and data exploration
  • Building and testing flexible and robust models that get results
  • Acquiring best practices to run a deployment feasibility check and test model in a cloned environment, before live production

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About the Authors: Shane Pederson & Matthew Mahowald

 
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Shane Pederson is a senior data scientist at Open Data Group and has extensive experience working as a statistical consultant in business, academics and government. He has a PhD in Statistics from the University of Minnesota.

 
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Matthew Mahowald is a Lead Data Scientist for Open Data Group. He holds a PhD in Mathematics from Northwestern University, with a focus on the geometry of string theory and topological field theory. At Open Data Group, Matthew focuses on developing and deploying machine learning models with FastScore.