90% of Model Success Hinges on Data Quality, Not Quantity

In the realm of machine learning, particularly for Vision LLM training, the quality of the dataset is paramount, often overshadowing the sheer volume of data. A recent project highlighted the use of Ray, a library designed for distributed data processing, to manage and enhance dataset preparation. Ray’s capabilities were crucial in handling large-scale offline batch inference, ensuring that the data fed into the model was both controlled and of high quality. This focus on data quality stems from the observation that while many new models are developed and shared, the specifics of dataset creation and curation are seldom disclosed. This lack of transparency underscores the importance of engineering efforts in dataset construction, as it directly impacts the performance and reliability of machine learning models.

Source: towardsdatascience.com