While both training data and models are essential to successful AI, the paradigm emphasis is shifting.
Data-centric AI and model-centric AI are two paradigms of designing, developing, and improving AI systems. They primarily differ in where they place their emphasis - on the data (the input and training sets) or on the model (the algorithms and techniques).
In the last several years, a model-centric approach was most common. This approach prioritizes the machine learning model's design and the algorithms used in creating these models. Improvements are typically made by adjusting the model's architecture, tweaking its parameters, or using more sophisticated algorithms. The underlying assumption is that the data is fixed or unalterable.
Today's models tend to be more powerful, harder to modify, and are more data hungry than ever before. This has given way to the idea that data is the differentiating factor of more successful AI initiatives, and so the focus in the industry has begun to shift from model-centric to data-centric.
A data-centric AI approach focuses on the quality of the data being fed into a system. It involves curating, cleansing, classifying, and labeling data more effectively. Rather than building increasingly complex models, data-centric AI assumes that the model is good enough, but the data could be better. The idea is to improve AI performance by ensuring high-quality, standarized, diverse, comprehensive, and representative data sets, and by refining the data labeling process.
One of the keys to the data-centric approach is a data preparation step consisting of identifying data inconsistencies, removing undesired redundant data, enriching data with third party datasets, and other data normalization processes.
This is where Interzoid can help. We can provide in-depth data analysis processes that address each of these challenges in-depth. We use our own Machine Learning capabilities to aid in the analysis and processing. Contact us at email@example.com if you are interested in a proof-of-concept where we can showcase our abilities for more success data-centric approaches to Artificial Intelligence.
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