{"id":50737,"date":"2022-05-11T00:00:00","date_gmt":"2022-05-11T00:00:00","guid":{"rendered":"https:\/\/www.techopedia.com\/data-centric-vs-model-centric-ai-the-key-to-improved-algorithms\/"},"modified":"2022-08-11T17:53:39","modified_gmt":"2022-08-11T17:53:39","slug":"data-centric-vs-model-centric-ai-the-key-to-improved-algorithms","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/what-is-data-centric-ai-and-why-do-we-need-it\/2\/34756","title":{"rendered":"Data-Centric vs. Model-Centric AI: The Key to Improved Algorithms"},"content":{"rendered":"

Nowadays, no matter what artificial intelligence<\/a> (AI) project we want to build, we need two main ingredients:<\/p>\n

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  1. A model.<\/li>\n
  2. Data.<\/li>\n<\/ol>\n

    Lots of progress has been made to develop effective models\u2014which has led AI to achieve many breakthroughs<\/a>. However, equivalent work has not been conducted in the realm of data, except making data sets<\/a> bigger.<\/p>\n

    While the progress towards conventional model-centric AI is making smaller differences, Andrew Ng<\/a> and many other leading scientists and academics are arguing to adopt data-centric AI<\/a>, which deals with the development of a new paradigm to systematically improve data quality.<\/p>\n

    Data-Centric Versus Model-Centric AI<\/span><\/h2>\n

    Data-centric AI differs from model-centric AI because, in the latter, the main focus is to develop and improve models and algorithms to achieve better performance on a given task. In other words, while model-centric AI treats data as a fixed artifact and focuses on improving AI models, data-centric AI frames models as a static artifact and focus on improving data quality. (Also read: <\/strong>What is Data Profiling & Why is it Important in Business Analytics?<\/strong><\/a>)<\/strong><\/p>\n

    Data is vital in AI; and adopting an approach to obtain good-quality data is crucial\u2014because useful data is not just error-prone and limited, but also very costly to obtain.<\/p>\n

    The key idea of data-centric AI is to handle data the same way we would high-quality materials when building a house: We spend relatively more time labeling, augmenting, managing and curating the data.<\/p>\n

    Why We Need Data-Centric AI<\/span><\/h2>\n

    The “mantra” of conventional model-centric AI is to optimize highly parameterized models with bigger data sets to achieve performance gains.<\/p>\n

    While this mantra works for many industries, such as media and advertising, it faces industries like healthcare<\/a> and manufacturing with many challenges. These include:<\/p>\n