Data-Centric AI — Data Collection and Augmentation Strategy

Author(s): Tan Pengshi Alvin

Originally published on Towards AI.

A comprehensive guide to data generation strategy for data-centric Machine Learning projects

Image by Dave Photoz on Unsplash

In the world of deep learning, complex models are data-hungry — in both quality and quantity — to perform well on inference at test time. However, in real-world Artificial Intelligence projects by commercial companies, Machine Learning Engineers seldom have the luxury of readily available training data. Especially for start-ups that begin with essentially zero data, the process of building up a dedicated data lake or data warehouse may be very time-consuming.

Moreover, in the data-centric approach to machine learning, the quality of training data collected is equally important to the performance of the deep learning model,… Read the full blog for free on Medium.

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Published via Towards AI