Build Better ML Models with These 5 QA Methods

The end result for every data labeling project is quality data - but how do you get there? There are several quality assurance workflow types but each has pros and cons when it comes to the quality and speed of data outputs. When you're evaluating data labeling providers or planning in-house processes, you should consider which QA workflow will work best for your business and data needs.

In this panel discussion, CloudFactory experts explore:

  • 5 QA workflows for data labeling teams
  • Pros and cons, tools, and staffing needs for each workflow
  • How each workflow impacts throughput and data quality
  • Answers to audience questions during the Q&A portion

Tell us about yourself

Courtney Wilson


Courtney Wilson is Director of Marketing at CloudFactory, where he works at the intersection of insights and content, helping our customers act strategically to solve their biggest data challenges.

Yasmeen Kashef


Yasmeen Kashef is a Client Success Manager at CloudFactory, where she is a trusted advisor to innovators who are building machine learning models and want to apply human-in-the-loop solutions to scale high quality training data for a competitive advantage.

Bill Heffelfinger


Bill Heffelfinger is the Head of Client Technology Solutions at CloudFactory, where he works with technical partners to build relationships, integrations, and joint solutions to support high-quality end-to-end data labeling operations.

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