What is Data-Centric AI ?
Data-centric AI encompasses methods and tools to systematically characterise, evaluate, and monitor the underlying data used to train and evaluate models. At the ML pipeline level, this means that the considerations at each stage should be informed in a data-driven manner. We term this a data-centric lens. Since data is the fuel for any ML system, we should keep a sharp focus on the data, yet rather than ignoring the model, we should leverage the data-driven insights as feedback to systematically improve the mode
Data-centric AI (DCAI) is a new class of AI technology that focuses on understanding, utilizing, and making decisions based on data. Before data-centric AI, AI was largely reliant on rules and heuristics. While these could be useful in some cases, they often led to suboptimal results or even errors when applied to new data sets.

Data-centric AI changes this by incorporating techniques from machine learning and big data analytics, allowing it to learn from data instead of relying on algorithms. As a result, it can make better decisions and provide more accurate results. It also has the potential to be much more scalable than traditional AI approaches. In the future, data-centric AI will likely become increasingly important as data sets grow in size and complexity.

Machine learning pioneer Andrew Ng argues that focusing on the quality of data fueling AI systems will help unlock its full power. "Andrew Ng"

Why Does Data-Centric AI Matter?
By adopting a Data-Centric AI approach, companies from diverse industries such as automotive, electronics, and medical device production have seen improvements in deploying AI and deep learning–based solutions in computer vision scenarios compared to traditional, rules-based implementations. Some improvements we’ve seen from adoption of a data-centric approach can make AI benefits accessible to most companies.
Build computer vision applications 10x faster Reduced time to deploy application Improved yield and accuracy


Resources
1- Dataversity