Best Practices: Partnerships between ML/AI and Data Labeling Companies


In this demo I will be using a geospatial analytics use case to discuss best practices between ML/AI and Data Labeling companies.
Key Takeaways:
- How to prepare for an engagement
- What questions to ask when vetting a company:
- Annotation Tool
- Data Labeling
- What metrics you should consider


Soo has been working with Computer Vision, Machine Learning Engineers, and Research Scientists, across industries to create training datasets for the last 4+ years. As a Solutions Architect at iMerit, she helps our clients by connecting the dots between the technical details of tooling, designing annotation workflows, and integrating a remote data labeling team for the execution. Previously, Soo served as the Data Operations Manager at a geospatial analytics startup where she built and scaled a Data Operations team from the ground up, leading a team 10 analysts.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

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