AI has the potential to revolutionize countless aspects of society and transform many facets of human existence. To do so, it requires both data and models. While model-building has been traditionally given more significance, Machine Learning expert Andrew Ng recently said it is time to focus on data instead, as there have already been numerous advancements in the models and algorithms. Data-centric AI is an artificial intelligence system set up around large amount of data, and it can be used to analyze complex data sets, make decisions, and extract insights. This type of AI is more reliable, accurate and flexible than model-centric AI.
To begin using a data-centric AI approach, it is important to understand the business problem and determine how data-centric AI can help address it. Then, high-quality data needs to be collected, cleaned, pre-processed, and stored in a data warehouse. Machine learning algorithms can then be used to analyze and understand the data, and make predictions. The insights from the data can then be incorporated for good decision-making. Finally, it is important to monitor and iterate the performance of the data-centric AI system, including updating the data, retraining the models if needed, and fine-tuning the system, according to the business requirements.
Data-centric AI offers many benefits, such as improved accuracy, flexibility, efficiency, and transparency. Most importantly, it allows organizations to make better use of vast amounts of data and improve the performance and accuracy of the AI system. Thus, shifting to a data-centric approach is essential to explore and utilize the strength of AI even better.