Data is the most critical input to produce high performing computer vision models, and yet data scientists need to manually rummage through the data for days to identify poor quality data that pulls model performance down.
This leads to biased models, lost opportunities for quick model performance improvements and a lot of wasted time for data scientists.
In this webinar, we will learn from leaders in the data science community (from Pinterest and Elbit Systems of America) about the criticality of fixing the data, best practices to do so, and how platforms like Galileo are pivotal in the workflow.