Your current data selection process may be limiting your models.
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Massive datasets come with obvious storage and compute costs. But the two biggest challenges are often hidden: Money and Time. With increasing data volumes, companies have a hard time dealing with the huge size.
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For any company, naively sampling small portions of large datasets (e.g. datasets of 1 million images or more) seems prudent, but overlooks immense value. Useful insights get buried in a haystack of unused data. For instance, how else do you overcome data imbalance if not with more data that triggers a balance?
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In this post, we鈥檒l unpack the two hidden costs of large datasets, and why current ways to leverage these datasets are expensive and inefficient.
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Table of Contents
- Introduction
- An exclusive model-centric approach is narrow
- Why does this matter?
- How to identify your hidden costs
- Conclusion
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1. Introduction
Most AI companies sit on massive amounts of un