Even with rising adoption, the failure rate for AI projects is incredibly high. How can we change that?
The basic components of all AI systems are data and model, both go hand in hand in producing desired results. We are here to talk about how the AI community has been biased towards putting more effort in the model, and see how it is not always the best approach. The value of data and its impact on the quality of ML-based solutions have, for sure, been underestimated so far, but this is changing Andrew NG recently announced data-centric AI competition where he covered the benefits of a bigger investment in data preparation with his team proving that investing in improved existing data quality is effective as collecting the triple amount of the data.
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