The Shift to Data-Centric AI: Why Aquaculture Needs Smarter Data, Not Just Smarter Models

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.

The Limitations of Model-Centric AI

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.

Aquaculture Needs to Focus on Data-Centric AI

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:

1. Better Data, Better Predictions
   In aquaculture, predictions about fish health, sea lice outbreaks, and environmental impacts rely on accurate, real-time data. Messy or incomplete data leads to flawed recommendations, no matter how advanced the model is. By cleaning, structuring, and enriching data, data-centric AI ensures that predictive models are learning from the best possible inputs—leading to more accurate predictions and better decision-making for farms.
2. Increased Access to Data
   The sheer volume of data available to farms has exploded. However, raw data alone doesn’t lead to insights. Without proper cleaning and processing, data can overwhelm rather than inform. Data-centric AI emphasizes structuring data so that it’s not only usable but valuable, making it easier for farms to extract meaningful insights and act on them.
3. Cost Efficiency
   Building more complex models often requires significant resources, both in terms of time and computational power. But by improving the quality of data, farms can achieve better results without needing to invest in more sophisticated (and expensive) AI models. Enhancing the data can lead to more impactful outcomes than simply increasing model complexity, making it a cost-effective approach for many farms.
4. Real-World Application
   In aquaculture, data is often messy, unstructured, or incomplete. Farms generate a wealth of information—from environmental metrics to health records—but much of this data is full of noise. In these scenarios, a data-centric approach becomes essential. Ensuring the data is relevant, accurate, and clean is critical for practical, real-world AI applications. It’s only through high-quality data that models can provide useful, actionable insights that benefit fish farms.
5. Explainability and Trust
   Data-centric AI also makes AI systems more transparent and trustworthy. When a model’s predictions are grounded in well-organized, high-quality data, it’s easier for farms to understand and trace the reasoning behind those predictions. This is particularly important when AI is used for decision-making in disease management and fish health—farms need to know that they can trust the insights provided by their technology.

Manolin’s Approach: Data-Centric AI for Modeling

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.

Why This Shift Matters for the Future of Aquaculture

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:

• Better AI Performance: With higher-quality data, AI models can perform better with less complexity, leading to more accurate and reliable predictions.
• Wider Adoption: Data-centric AI democratizes the use of AI by making it more accessible to farms that may not have the resources to build cutting-edge models. It’s about better data, not bigger models.
• Increased Trust: When farms can trust that the data feeding into their models is accurate, it builds confidence in the insights they’re receiving—leading to more effective decision-making.

The Power of Smarter Aquaculture Data

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.