Thought leaders in the AI space such as Andrew Ng have been advocating for a shift from model-centric to data-centric AI. The idea behind this campaign is that AI models can be only marginally improved through tweaks in the algorithm but considerable change can only be achieved by using high-quality data. However, what does “high-quality data” mean and how do we go about ensuring the quality, diversity, and consistency of our dataset? In this talk, we will discuss the practice of collecting and annotating data for your computer vision models and making sure the dataset you are using is representative and free of harmful biases.
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