Artificial intelligence (AI) is rapidly transforming industries, and aquaculture is no exception. As farms become more data-driven, there’s been a major shift from model-centric AI to data-centric AI. In this shift, we’re seeing that improving the quality, structure, and usability of data is far more impactful than endlessly tweaking and optimizing AI models. The aquaculture industry is moving toward embracing data-centric approaches, ensuring farms get the most value from the data they collect.
But why is this shift happening, and what does it mean for aquaculture and fish farms? Let’s explore why data-centric AI is gaining ground and why it’s so important for the future of aquaculture predictions and fish health management.
Traditionally, model-centric AI focused on refining algorithms to improve predictions. The thinking was simple: if you build a better model, you’ll get better results. This approach worked well when the models were in their infancy, and making incremental improvements led to big gains in performance.
However, as AI has become more sophisticated, the limitations of this approach have become clear. Many of the performance barriers now lie in the data feeding into the models, not in the models themselves. No matter how advanced a model becomes, it can only be as good as the data it’s trained on. As the industry has advanced, AI developers began to notice diminishing returns from further tweaking models alone.
In many cases, the real bottleneck is the quality, consistency, and relevance of the data itself.
Data-centric AI flips the focus, placing data quality at the center of AI development. Instead of endlessly tuning models, the idea is to improve the data pipeline—ensuring the data that feeds into the models is clean, well-labeled, and comprehensive. This approach unlocks better performance from models without the need for constant model optimization.
Here’s why this shift is particularly relevant for aquaculture:
At Manolin, we’ve long understood the importance of data quality in delivering accurate, actionable predictions to fish farms. Our platform uses a data-centric approach to our modeling, where clean, structured data from farms is fed into our AI systems to predict disease risks and optimize fish health management.
For example, our self-healing data modules automatically clean and optimize data, ensuring farms are working with the most accurate and up-to-date information. This is critical for making informed decisions about fish health and preventing disease outbreaks.
By integrating high-quality data from multiple sources—fish health records, environmental conditions, and historical trends—Manolin’s platform can deliver more precise predictions across key fish welfare metrics that matter most to managers. Our focus isn’t just on building more complex models; it’s on improving the data pipeline to provide farms with better, more trustworthy insights.
The shift toward data-centric AI is critical for the future of aquaculture because it addresses some of the core challenges farms face: making sense of complex, messy data in a way that leads to actionable insights. As data collection continues to grow, farms will need more sophisticated ways of processing and understanding that data. This is where data-centric approaches will continue to make an impact.
Key benefits of this shift include:
The shift from model-centric to data-centric AI marks a major turning point for aquaculture. As farms continue to collect more data, the focus is shifting toward ensuring that this data is clean, structured, and useful. At Manolin, we’re leading the way by embracing data-centric approaches that deliver better, more reliable predictions for disease management, fish health, and farm optimization.
The future of aquaculture isn’t about building more complex models—it’s about harnessing the power of smarter data. As data-centric AI continues to evolve, it’s clear that the quality of your data will be the key to unlocking better insights and outcomes for fish farms around the world.