Data-Centric AI

The importance of quality data has not received the attention it deserves. According to Google researchers, conventional approaches to AI development that fail to underscore the role of data suffer often from the negative downstream effects of poor-quality data.

While current AI development strategies focus primarily on the model’s code, the demand for accelerated innovation in artificial intelligence has catalyzed a paradigm shift into a data-centric approach, which will allow companies to unlock even more value out of their machine learning models.

How Did Model-Centric AI Work?

In the traditional, model-centric approach to AI development, teams would focus almost entirely on building the model and treat the data like a static variable. This model-centric approach worked in the early years of AI when model architecture was relatively simple.

But today, the inner workings of AI models are becoming more complicated, and developers are finding more value studying the input data instead and taking a more “black box” approach to model tuning. Working with data is easier and provides an avenue for subject-matter experts to add their input.

How Does Data-Centric AI Improve Upon Traditional Methods?

In contrast to traditional model-based AI that relies heavily on pre-set rules and potentially inaccurate generalizations, data-centric AI focuses on understanding the data and making decisions off it accordingly.

In a data-centric project, teams:

  • Gather the relevant data,
  • Form data sets after cleaning up and labeling the data,
  • Train the model with those sets,
  • Deploy the model in the field,
  • And monitor performance.

Based on the AI’s performance, those teams repeat the cycle from the beginning to make iterative improvements or respond to changes in the data.

Data-Centric vs. Model-Centric

Switching from model-centric to data-centric development not only improves the accuracy and performance of machine learning models but also streamlines implementation and reduces time-to-deployment.

Companies must push for this new approach to ensure:

  • Better model performance– Data-centric methods are known to generate models with higher accuracy rates than model-centric ones.
  • Faster implementation– By focusing more on quality data, developers can form the AI into a model more applicable for the intended use case. Otherwise, they waste time adjusting a model that’s working on an unstable foundation of poor data.
  • Model flexibility– A model-centric AI often struggles with new data sets. For example, a manufacturing plant using a model-centric computer vision AI to check for defects in the final product may receive more erroneous results when lighting conditions in the factory change.
  • A stronger focus on data– Switching to a data-centric approach also creates a new focus on AI data management. Going back to the defect detection example, proper data management ensures the AI can correctly categorize a significant defect from a inconsequential anomaly.
  • Internal collaboration– Since the entire company contributes to producing quality data, a data-centric approach brings together developers, specialized staff, and management to collaborate on model production.
  • Future-proof scalability– A general push for data-centric strategies also makes AI solutions more scalable and standardized. When individual teams work on disparate AI deployments, it becomes challenging to collaborate since no standard workflow exists.

It’s worth noting that data-centric and model-centric methodologies are not mutually exclusive, and successful AI developers often combine quality data with well-built models.

How Data-Centric AI Development Contributes to More Productive Machine Learning Operations

Companies today are dedicated to optimizing machine learning operations (MLOps), the business function of deploying and maintaining AI models. And a transition into data-centric ML will be a key component of MLOps practices in the foreseeable future.

Data-centric MLOps matters because:

  • AI performance relies heavily on quality data, and sloppy data sets can result in faulty predictions.
  • When market conditions change, input data sets are the first to shift. A data-centric development approach always incorporates the new data as quickly as possible and ensures the model constantly adapts.
  • It makes understanding the data easier. MLOps teams can quickly identify patterns, relationships, and outliers when they focus on data and can come up with new opportunities for improvement and automation.

A data-centric approach to MLOps is universally impactful for any industry that utilizes AI, ranging from finance to healthcare and manufacturing.