Don't miss an insight. Subscribe to Techopedia for free.


What's the difference between model-driven AI and data-driven AI?

By Dr. Tehseen Zia | Last updated: January 30, 2023

Artificial Intelligence systems essentially involve two main ingredients: Code and Data.

The code reflects AI model or algorithm which is trained using the data. The conventional model-centric AI focuses on improving code to achieve better results given a fixed set of data. AI developers generally consider the training datasets from which their code is learning as a collection of ground-truth labels, and their AI model is made to fit that labeled training data. Thus, this approach generally assumes the training data as external from the AI development process.

On the other hand, data-centric AI aims to improve data quality to achieve better outcomes by treating code as an unchangeable entity. In other words, while model-centric AI deals with developing or improving the AI model or algorithm, data-centric AI deals with the labeling, augmenting, managing and curating of data. Data-centric AI may seem to be the pre-processing of data, however, it emphasizes an iterative AI life-cycle consisting of data collection, model training and analyzing errors.

In model-centric AI, we spend relatively more time on optimizing an AI model whereas in data-centric AI, we spend rather more time on data quality improvement. In model-centric, we aim to find the most suitable AI model or an optimization technique for a given problem, whereas in data-centric we aim to find inconsistencies in the collected data for a given problem.

Nowadays, model-centric AI tends to optimize bigger AI models on large-scale datasets which therefore require large-scale datasets and lots of computing resources, whereas data-centric AI may require domain knowledge or experts to find inconsistencies in data.

Though most data-centric AI ideas already exist as conventional wisdom in the AI community, data-centric AI aims to build a systematic approach and the tools needed to facilitate this process.

Share this Q&A

  • Facebook
  • LinkedIn
  • Twitter


Artificial Intelligence Machine Learning Data Science

Written by Dr. Tehseen Zia | Assistant Professor at Comsats University Islamabad

Profile Picture of Dr. Tehseen Zia

Dr. Tehseen Zia has Doctorate and more than 10 years of post-Doctorate research experience in Artificial Intelligence (AI). He is assistant professor and leads AI research at Comsats University Islamabad, and co-principle investigator in National Center of Artificial Intelligence Pakistan. In the past, he has worked as research consultant on European Union funded AI project Dream4cars.

More Q&As from our experts

Related Terms

Related Articles

Term of the Day

Risk Management

Risk management is the process of identifying, assessing, and prioritizing risks to an organization's capital and...
Read Full Term

Tech moves fast! Stay ahead of the curve with Techopedia!

Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.

Go back to top