Data-Centric AI Vs. Model-Centric AI
How much more data do we need to make this model work reliably? – This question often cripples AI engineers in their efforts to create a practical machine learning solution. The quest to build effective AI models is never-ending. But it is now becoming more data-centric than ever before.
That brings us to an even more basic question – What are the fundamental elements of a working AI solution?
In the traditional approach, machine learning practitioners primarily show interest in improving the model to make the solution more effective. This strategy is called the model-centric approach. Let’s discuss this approach in detail to understand the need for the data-centric approach.
In the model-centric approach of AI, developers give primary importance to the ML algorithm and the model to build the machine learning solution. It acts as the sole means to improve the model’s performance. Even though experts now favor the data-centric approach, AI models have developed splendidly with model centrism.
Here are a few features of this approach.
Traditionally, data science teams download a static dataset from a source. It is then processed, cleaned, and stored in the database. After that, it does not get much consideration as data collection & processing is considered a one-time thing.
This approach gives a lot of emphasis on data quantity to make the models work. Thus, although we get a lot of data to work with, the benefit does not match the effort.
The primary focus is on the algorithm and the model to improve the predictive performance of the AI model. Various algorithms are experimented with, and hyperparameters are tuned progressively to achieve the needed improvement.
Even though AI has made inspiring progress in the last decade, some problems have become painfully clear with the current approach.
Some of the drawbacks of using the model-centric approach are –
There is a limit to the progress you can achieve by only working on your algorithms. After a while, the performance is bound to reach a peak and converge. However, if you work on engineering your data for quality, you can easily outperform your past benchmarks.
When you are especially concerned about the model only, you fail to notice the peculiarities in your data. That leads to the ignorance of data skewness, under-representations, and biases that creep silently into the model.
The traditional approach is data volume intensive. It means that you need a lot of data to bring desired results. As the volume of data you work with increases, you need more resources to store and handle this data. This scenario increases the total operational costs of a project.
Having a large amount of training data for every use case is not always possible. That makes it hard to solve practical problems using AI.
Data-centric AI is the approach to developing AI models where you focus on engineering your data to improve your model’s performance.
Now that the machine learning experts have hit a roadblock with model-centricity, they are now starting to talk more about the data. In the data-centric approach, data gets the primary importance.
A few of the essential features of this approach are –
Data centricity prioritizes data quality over its quantity. The working belief is that a model with less training data, but superior data quality, can perform on par with a model trained on a large data volume.
In the data-centric method, data processing steps like labeling, augmenting, cleaning, etc., are given much more time and resources than in the model-centric approach.
Data processing isn’t a one-time thing for those who follow data centrism. Data keeps evolving with time through feedback from the model and other information that data engineers keep discovering throughout the project lifecycle. Thus, data is constantly improved to improve the model’s performance.
AI is solving problems in different domains and industries.
