Data-Centric AI is an artificial intelligence development paradigm that prioritizes improving the quality, consistency, and structure of the dataset over iterative algorithm tuning to enhance model performance. Its purpose is to build more reliable and robust AI systems, which is particularly critical in data-intensive and volatile domains such as crypto trading.
Mechanism
This approach involves rigorous, systematic processes for data collection, cleaning, labeling, and augmentation, focusing on identifying and rectifying errors, biases, or inconsistencies within the training data itself. Rather than primarily adjusting model architectures, the effort concentrates on curating, validating, and enriching the information provided to the models, ensuring high fidelity and direct relevance to the specific prediction or decision task.
Methodology
The methodology centers on establishing a disciplined data pipeline, incorporating tools for data governance, versioning, and quality assurance, often leveraging human-in-the-loop processes for expert annotation and verification. In crypto, this translates to meticulously preparing market data, transaction records, and sentiment indicators to train predictive models for request for quote (RFQ) pricing or smart trading, aiming for superior accuracy and reduced model drift in dynamic environments.
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