Insider Tips on Data Annotation Project Management
Collaboration & Project Management for data scientists and ML engineers
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85% of ALL machine learning projects NEVER reach real-world deployment – most often due to a lack of excellent-quality training data!
To be fair, getting this excellent-quality training data through data annotation is no picnic. A labeling project is much more complex than a normal ML project. Scoping, processes, teams, timelines: the opportunities to fail are numerous and the cost in time and budget is huge.
As a fellow machine learning engineer, and as the former Head of AI at BNP Paribas, our co-founder and CTO Edouard knows where you stand. Before co-founding Kili Technology, he built one of the most advanced AI labs in Europe from the ground and faced the same challenge.
That’s why he held this session on how to adopt an industry-leading approach to managing and delivering successful data annotation projects.
By watching the replay, you'll learn:
- his learnings on the most common reasons why your data annotation project doesn't perform;
- and his end-to-end recipe to scope and execute a data annotation project smoothly.