That’s one of the reasons why the data-centric approach is starting to take over the front, bringing a fresh perspective on how we form artificial intelligence systems. In the near future, this approach will likely take the lead, improving the accuracy of AI predictions and, thus, contributing to the rapid growth of various industries or even, who knows, facilitating some scientific milestones and discoveries that have been inaccessible so far due to inefficient data management. So, how does the data-centric AI work, and how to employ it in your organization?
What is data-centric AI?
In a traditional approach, the focus is always on the code and the model itself. The developer’s job is to produce the best model for the dataset. However, it turns out not to be the best path for unleashing the full potential of artificial intelligence. AI can do more harm than good without the right data. If you train the model with poorly selected and prepared datasets, it can provide you with random results. Machine learning is worth what you put into it, and companies are starting to understand it, switching to the data-centric approach, which puts data in the limelight.
This way, the model can learn for the exact purpose and avoid hallucinations or inaccuracies that often come hand in hand with the lousy datasets. Data centric approach puts AI data quality first, regardless of its specifics. More time and effort is put into data selection, eliminating the noisy and inconsistent data that could have a negative impact on overall accuracy. It’s not the time lost since usually, after such a thorough process, the model performs much better from scratch, eliminating later efforts to improve its results.
Benefits of data centric AI
Data-centric approach helps reducethe unnecessary trial-and-error parts of the development process that usually occur when the model isn’t trained with the right dataset. The team doesn’t need to turn back to the previous stages of development and look for potential errors, and thus, the whole process progresses much faster. That also means potential savings and higher security for your budget. In the end, artificial intelligence projects usually generate a lot of expenses, and with data-centric AI, you can cut them significantly.
At the same time, as an approach that leads to fewer errors, data-centric AI can provide you with better safety, which is crucial in certain kinds of projects. A good example is the automatic vehicle industry, where we often hear of the algorithms failing and leading to crashes. However, it is usually about the dataset quality.
Also, more and more companies are constructing the models in a data centric way, trying to understand what type of data structure can lead to better accuracy. An example from an AV world – it is increasingly common to switch to simpler models that analyze the features of an object instead of trying to identify the object itself. This way, the model that navigates the car can react to the obstacles quicker in any conditions.
When is it worth employing data centric artificial intelligence?
The data centric approach is particularly beneficial when it comes to computer-vision-based systems, whether for predictive maintenance, defect detection, or self-driving vehicles. Noisy visual data can seriously impair the system’s capability of categorizing objects based on their visual features. That could lead to lower product quality or even security issues. Data centric approach protects the business, the workplace, and the end user, preventing issues on any product journey stage.