90% of Machine Learning Success Hinges on Data Quality

In the realm of machine learning, the quality of data is paramount. A seasoned machine learning engineer shares insights from years of developing an image classification system. The key takeaway? Data quality is not just important; it’s the foundation of model success. The engineer emphasizes that while everyone knows good data is crucial, defining, building, and maintaining this quality is challenging. The focus should be on curating data before even considering the model. This approach helps avoid the common pitfall of “garbage in, garbage out,” ensuring higher model accuracy and demonstrating tangible business value. The series of articles will explore the care and feeding of a multi-class, single-label image classification app, focusing on data management techniques rather than coding specifics.

Source: towardsdatascience.com