Understanding Data-Centric AI
Imagine a programming approach that chooses to prioritize data over codes - this is Data-Centric AI. Despite the advancements in AI technologies and their growing adoption across diverse sectors, a transformation towards a data-centric perspective is necessary to fully exploit the potential of AI.
Multiple sectors, from electronics to automotive, have reaped benefits from transitioning to AI and deep-learning centered on data, especially in the manufacturing scenarios, as against the conventional rules-based systems.
Limitations of data
Debate on labeling - AI systems in sectors such as manufacturing and pharmaceuticals are used to pinpoint product defects. But interestingly, there may be disagreements among well-informed individuals on the correct labels of faults, such as whether a pill is "chipped" or "scratched". This creates ambiguity in the systems, hindering performance. Similarly, differences in how hospitals categorize digital records can be a problem for AI.
Misplaced emphasis on big data - Many believe that more is always better when it comes to data. However, for fields like healthcare, there's not always a large quantity of data available and in some cases, lesser volumes of high-quality data are sufficient.
Ad hoc data curation - Many a time, data is replete with errors. Correcting these errors often falls on individuals, whose skills or lack thereof can greatly impact the accuracy of the results.
Developer reliance - AI model's performance improvement heavily relies upon the developer. For example, the developer must collaborate with experts to correctly identify faults. Model maintenance and adjustment to changing circumstances can cause deployment difficulties and delays.
Data-Centric and Model-Centric
In the model-centric tactic, the dataset is often viewed as external to the primary AI development course. Here, data scientists see the training data mostly as a collection of tags and accordingly, build their ML model.
However, the shift to a data-centric approach signifies a significant change in the concentration of the machine-learning community. It implies investing more time in effective data labeling and management, rather than focusing solely on the model.
The success of AI is contingent on both: a properly designed model and adequate data.
Advantages of Data-Centric Approach
A data-centric tactic primarily involves building AI systems with high-quality data, with the objective of the data accurately reflecting what the AI is meant to learn. This process reduces the excessive guesswork involved in model development in the absence of consistent data.
Throughout this approach, managers, experts, and developers can cooperate to:
- mutually agree on faults and labels
- construct a model
- evaluate outcomes
- implement further improvements
One of the major advantages of a data-centric business model is the opportunity to collaborate more closely during the AI system's development and directly influence the data being used. This often leads to reduced development time by minimizing the back-and-forth between teams.
More so, a data-centric approach enables teams to develop consistent methodologies for capturing and categorizing images, as well as training, updating, and enhancing models. The learnings from former projects can then be used to swiftly develop new initiatives, further adding to the benefits of a data-centric methodology.