As AI has grown in popularity over the past decade, practitioners have concentrated on gathering as much data as possible, classifying it, preparing it for usage, and then iterating on model architectures and hyper-parameters to attain our desired objectives. While dealing with all of this data has long been known as laborious and time-consuming, it has typically been seen as an upfront, one-time step we take before entering into the essential modeling phase of machine learning. Data quality concerns
, label noise, model drift, and other biases are all addressed in the same way: by collecting and labeling more data, followed by additional model iterations.
The foregoing technique has worked successfully for firms with unlimited resources or strategic challenges. It doesn't work well for machine learning's long-tail issues, particularly those with fewer users and little training data.
The discovery that the prevailing method of deep learning doesn't "scale down" to industry challenges has given birth to a new "trend" in the area termed "Data-Centric AI."
Implementing a Data-Centric Approach for AI Development
Leverage MLOps Practices
Data-centric AI prioritizes data over models. Model selection, hyper-parameter tuning, experiment tracking, deployment, and monitoring take time. Data-centric approaches emphasize automating and simplifying ML lifecycle operations.
Standardizing and automating model-building requires MLOps. MLOps automates machine learning lifecycle management pipelines.
An organizational structure improves communication and cooperation.
Involve Domain Expertise
Data-centric AI development requires domain-specific datasets
. Data scientists can overlook intricacies in various sectors, business processes, or even the same domain. Domain experts can give ground truth for the AI use case and verify whether the dataset truly portrays the situation.
Complete and Accurate Data
Data gaps cause misleading results. It's crucial to have a training dataset that correctly depicts the underlying real-world phenomenon. Data augmentation
or creating synthetic data might be helpful if gathering comprehensive and representative data is costly or challenging for your use case.