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November 5, 2018
Date: Sep 15, 2020
One of the most important phases of a machine learning project is data collection and annotation. Together, they account for at least 40% of the effort to get a project from conception to deployment.
In this presentation, we will look at a real-life case study for a large-scale data collection and annotation project that involved mathematical formulas, diagrams, and handwritten text in multiple languages, including Chinese, Korean and Japanese.
To deliver the data, Lionbridge leveraged its distributed network of annotators, involving hundreds of specialists around the world.
Join this 60-minute presentation to learn
What methods for data collection work best
How to ensure the diversity of your dataset
The importance of data quality
How to deliver your ML project on time and on budget
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