Unveiling the true potential of artificial intelligence by shifting from model-centric to data-centric AI

Artificial intelligence is the ability of computers and machines to perform tasks that would generally require human intelligence. AI has the potential to revolutionize myriad features of society and transform many ways of human existence. A basic artificial intelligence system needs both data and models to work perfectly. Both work simultaneously to achieve the desired result. People familiar with AI would agree that more importance is attached to modelling. But well-known machine learning expert Andrew NG shared his thoughts at a recent conference, saying that now is the time to focus more on data as there have already been many advances in models and algorithms. Investing time and effort in data would help uncover the true value of AI in various sectors such as healthcare, government, technology and manufacturing.

Model-centric AI

Model-centric AI is an artificial intelligence system built around a specific machine learning model or algorithm. It relies on the model to make predictions or generate an outcome. Most of these systems are designed to optimize the performance of the model. This AI approach is often used when trying to achieve a specific performance goal, e.g. B. high accuracy or high precision in a classification task.

Model-centric AI can be imaginative and effective to solve a problem that requires analysis, such as: B. Speech or image recognition. These are automated and very convenient to implement as no manual programming is required. However, a model-centric system may not be as flexible or adaptable as it is designed to perform a specific task, and it may be difficult to adapt to new scenarios.

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Data-centric AI

Data-centric AI can be defined as an artificial intelligence-based system built around vast amounts of data and using that data to learn and make decisions. These systems primarily use machine learning techniques to analyze trends in the data, generate insights, understand patterns, and make predictions. This type of AI is commonly used when it comes to analyzing and understanding complex data sets or making predictions or decisions based on data. It can learn and improve significantly over time with exposure to more data.

importance of data

Data is crucial for developing and working with artificial intelligence (AI) systems. Without access to high-quality data, it is impossible to build effective AI systems, as data is a key feature in AI development and deployment. For an AI system to learn and make decisions, it needs to be trained on a large amount of updated data. AI uses this data to uncover patterns and insights that may not be obvious to humans. For example, an AI system could be trained on medical record data and be able to recognize early warning signs of a deadly disease.

types of data

  1. Structured Data – Data that is traditionally organized in a table or spreadsheet in a structured manner, in the form of rows and columns.
  2. Unstructured Data – Data containing a variety of things, from images and audio to emails or text messages, collected in a variety of formats in a disorganized manner.
  3. Nominal data – They represent categories or labels. It is called nominal because it is not ordered or ranked in any way. For example – non-numeric variables represent gender, item type, etc.
  4. Ordinal data – They represent categories that have a natural order or an associated ranking. For example – a list of grades like A, A+, B, etc.
  5. Discrete data – They can only take on a specific set of values. Discrete dates are often used to represent countable items. For example – the number of pages in a novel, the number of chairs in a room, etc.
  6. Continuous data – Continuous data is a data type that can take on any value within a specified range. For example – a person’s height and weight, temperature, length, width, etc.
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Why is the move to data-centric AI important?

Everyone is inundated with a multitude of data such as scientific data, medical data, financial data and so on. This data is collected daily and analyzing this information is essential. The rapidly growing, massive amount of data collected and stored in large data stores has exceeded human ability to understand without powerful tools. Data-centric AI allows the system to adapt and evolve as the data changes. It enables companies to make better use of huge amounts of data. It improves the effectiveness of artificial intelligence systems.

  1. It greatly improves the performance and accuracy of the model.
  2. Data directly influences approach; therefore, development takes less time.
  3. The method leads to up-to-date solutions as it accommodates the changing data.
  4. There is more transparency as the trends and patterns are explainable by looking at the data.

Steps for moving to a data-centric AI approach

  1. Understand the business problem and determine how data-centric AI can help solve it.
  2. Collect, cleanse and pre-process high-quality data and store it in a data warehouse.
  3. Using machine learning algorithms to analyze and understand the data and make predictions.
  4. Incorporate insights from the data for good decision making.
  5. Monitoring and iterating the performance of the data-centric AI system, including updating the data, retraining the models when necessary, fine-tuning the system, etc. according to the business needs.


Data-centric AI can provide many benefits such as: B. improved accuracy, flexibility, efficiency and transparency. These systems are even more reliable because they can learn from large amounts of data and make predictions based on patterns and trends that humans may not be immediately aware of. They learn and improve over time as new data becomes available. Therefore, shifting to a data-centric approach is the need of the hour to better explore and harness the power of AI.

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  • https://ai-infrastructure.org/moving-from-model-centric-to-data-centric-ai-2/
  • https://mitsloan.mit.edu/ideas-made-to-matter/why-its-time-data-centric-artificial-intelligence
  • https://towardsdatascience.com/from-model-centric-to-data-centric-artificial-intelligence-77e423f3f593
  • https://neptune.ai/blog/data-centric-vs-model-centric-machine-learning
  • https://landing.ai/data-centric-ai/
  • https://www.picsellia.com/post/data-centric-ai-vs-model
  • https://medium.com/analytics-vidhya/moving-from-model-centric-to-data-centric-approach-1468fb5dbafb
  • https://www.analyticsinsight.net/from-model-centric-to-data-centric-how-the-ai-ecosystem-is-moving/
  • https://mitsloan.mit.edu/ideas-made-to-matter/why-its-time-data-centric-artificial-intelligence

Tanya Malhotra is a senior at University of Petroleum & Energy Studies, Dehradun pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking skills and a passionate interest in learning new skills, leading groups and managing work in an organized manner.