The HOLiFOOD infrastructure

The HOLiFOOD infrastructure is a sophisticated, AI-driven platform specifically designed to enhance food safety measures. This platform represents an innovative integration of AI models and robust data management systems, aimed at providing advanced predictive analytics essential for contemporary food safety practices.

 

Components of the HOLiFOOD Infrastructure

• Data Layer
  • This foundational layer is tasked with managing a wide array of data types. It encompasses both structured and unstructured data segments.
  • Structured Data Management: Utilizes relational database management systems for efficient storage and retrieval of structured data originating from various work packages.
  • Unstructured Data Management: Leverages Elastic search databases for handling unstructured data forms such as laboratory test results and public food safety announcements.
  • This layer plays a crucial role in gathering, cleaning, and preparing data, which forms the backbone for training sophisticated AI models.
• AI Management Layer
  • Serving as the central hub of the infrastructure, this layer is dedicated to AI Model Management.
  • It effectively hosts and executes trained AI models, leveraging the capabilities of Creme’s infrastructure to ensure these models are always ready for deployment.
  • This layer is instrumental in ensuring that the transition from data processing to AI model implementation is seamless and efficient.
• AI Services Layer
  • Houses critical components such as the Timeseries & Forecasting Engine, integral to the platform’s predictive capabilities.
  • The Predictions Engine, part of this layer, utilizes advanced forecasting algorithms to analyze incident data as time series.
  • This functionality allows the AI models to not just analyze past and present data but also to project future trends and incident numbers, enhancing the platform’s predictive accuracy and utility.
• Interface Layer
  • Positioned as the uppermost layer, it serves as the primary point of interaction with external systems.
  • Includes innovative components like the Federated Querying Module, which enables a distributed querying mechanism across different data sources, thus enhancing the platform’s reach and efficiency.
  • An API Gateway is also included, ensuring secure and managed access to the services and data offered by the underlying layers.

 

Elaborate on Stakeholder Requirements

• Internal Stakeholder Requirements
  • Derived from detailed internal interviews with leaders of WPs 1 and 2, focusing on identifying the essential requirements for the technical development of the HOLiFOOD infrastructure.
  • A structured approach to these interviews was employed to comprehensively understand specific needs, ranging from data types, storage, and access requirements to handling sensitive data.
• External Stakeholders (Living Labs)
  • Insights from the Synergy Days event provided valuable information about the expectations and requirements of external stakeholders regarding AI tools in food safety.
  • These insights included a strong inclination towards AI-driven optimization, efficient standardization, and cost-effective solutions in food safety.
  • The feedback pointed out the technical necessities, like high-performance computing infrastructure and qualified staff, and highlighted the challenges, such as digital resource availability and financial constraints.

 

Detailed Overview of HOLiFOOD Infrastructure Modules

• Data Layer
  • Employs AWS for its robust, secure cloud computing environment, benefiting from comprehensive data encryption both at rest and in transit.
  • The use of AWS S3 ensures the integrity and safety of data with advanced security measures like multi-layered physical security and stringent access control.
  • The layer’s design adheres to European data protection regulations, ensuring compliance with standards like GDPR.
• AI Management Layer
  • The Creme Global Platform integrates and publishes AI models, providing a secure and scalable environment for hosting and running AI models.
  • This platform enables the integration of a range of predictive models, automated execution, and visualization through designed dashboards.
  • The data management services complement the modelling services by providing secure data storage and efficient data integration from multiple sources.
• AI Services Layer
  • Features Agroknow’s Time Series Management & Forecasting Engine which applies forecasting algorithms to treat incident data as time series.
  • This engine plays a pivotal role in predicting future trends and incident numbers, thus significantly enhancing the predictive capabilities within the HOLiFOOD platform.
  • The engine’s architecture allows for batch predictions and storing them in persistent storage for on-demand access, emphasizing the importance of batch processing over online inference.

 

Conclusion

In summary, the HOLiFOOD infrastructure, through its comprehensive and multi-layered approach, represents a significant advancement in the field of food safety.

Its emphasis on seamless integration of AI models, efficient data management, and stakeholder-centric design positions it as a cutting-edge solution in predictive analytics for food safety.

For HOLiFOOD and similar infrastructures to achieve their full potential, they must continue to evolve with high-performance capabilities, incorporate extensive training and capacity building for staff, and include mechanisms to address data availability, standardization, and sensitive data handling while ensuring reliability and trust in AI-driven processes.