How a University of Bath Intern is Leading Manolin’s Data Towards Self-Healing Excellence

At Manolin, our success is deeply rooted in fostering innovation and nurturing talent. This summer, we had the privilege of welcoming Jonah Kaplan, a University of Bath computer science master's student, as an intern. Jonah brought a strong skill set that perfectly aligned with our ongoing efforts to enhance our data processes. By contributing to the development of a self-healing data module, Jonah played a vital role in our mission to maintain the highest standards of data quality and efficiency.

A Summer of Growth: Jonah’s Internship Experience

Reflecting on his time at Manolin, Jonah describes his internship as a transformative experience. "Interning for Manolin was a great experience that not only helped me develop my skills but also allowed me to do worthwhile work with the team," he shared. Jonah particularly enjoyed connecting with the team, learning from their expertise, and contributing to projects that have a direct impact on our operations.

Jonah’s experience highlights the value we place on collaboration and knowledge-sharing. At Manolin, internships are more than just a learning opportunity—they are a chance to make a real difference by contributing to meaningful projects.

Innovating with the Self-Healing Data Module

When Jonah joined our team for the summer, we identified his potential to contribute to a critical project: the development of a self-healing data module. This module was designed to ensure the integrity and accuracy of the vast amounts of data we collect. Jonah’s task was to help build and refine a system that would allow us to identify and address data anomalies more effectively.

Jonah explains, "I contributed to the creation of a module to profile and validate our data catalog, ensuring the integrity of the information we collect. Its main purpose is to visually identify outliers and recalculate their values for comparison." Before this module, our team used semi-automated processes to identify and resolve outliers. As our data grew, this approach became increasingly cumbersome and time-consuming. Jonah's work has streamlined the process, enabling large-scale data validation at a glance.

The development process was iterative, with Jonah working closely with our team to refine and expand the module’s features. His efforts in optimizing the module to handle large datasets efficiently were crucial to ensuring that it was not only functional but also scalable and flexible—qualities that are essential for the dynamic nature of our work at Manolin.

“Jonah’s contributions to this project were invaluable. His work helped us take an existing concept and turn it into a fully functional system that’s now an integral part of our data management process,” said Manolin CTO, John Costantino.

Impact on Manolin’s Data Models

The self-healing data module has already begun to demonstrate its value in improving the accuracy and efficiency of our data models. By identifying and correcting anomalous data, the module plays a crucial role in maintaining the high quality of our datasets. Jonah notes, "This module is a key step in ensuring the quality of the data is as high as possible. This means it can lead to the accuracy of our models improving."

During the development and testing phases, Jonah’s tool quickly began identifying anomalous results, allowing us to quickly adjust our algorithms to eliminate such data and prevent similar issues from occurring in the future. The module’s ability to pinpoint specific issues in a sea of data within a fraction of a second has positioned Manolin to roll out new models and insights with greater confidence.

Looking ahead, Jonah envisions this module playing an even more significant role in our operations. "In the long term, I think this module will help speed up testing of new features, which will allow us to roll out new models and insights faster and with more confidence," he said.

Personal and Professional Growth

Jonah’s work on the self-healing data module was not just a technical achievement—it was a major milestone in his personal and professional development. "Having experience like this will not only help me with my degree but also make it easier to find a job when I graduate," Jonah reflected. His internship at Manolin provided him with hands-on experience in SQL, data processing with Python, and the opportunity to engage in weekly stand-up meetings, gaining valuable insights into communication and project management within a company.

Jonah’s dedication and curiosity have undoubtedly set him up for future success, and we are proud to have been part of his journey.

Costantino adds, “Jonah exceeded expectations. To develop what he did in 6 weeks speaks volumes to his unending curiosity and engineering mindset. I feel honored to be able to work alongside him.”

Looking Ahead: Future Applications and Goals

The self-healing data module is just the beginning for Manolin. Costantino sees potential for further enhancements, particularly in improving the module’s frontend to increase ease of use and expand its functionality. He envisions the module being integrated into a larger system that continuously validates, analyzes, and corrects errors—a fully automated self-healing system that would ensure the ongoing improvement of our data quality.

Jonah’s advice to future interns and developers working on similar projects is clear: "Always keep flexibility and scalability in mind while developing. This allows you to quickly change and prototype without having to remove any progress you have made." His forward-thinking approach embodies the innovative spirit we value at Manolin.

Manolin’s Commitment to Innovation

Manolin’s advancement in its self-healing data module is a testament to the company’s dedication to innovation and excellence in aquaculture data intelligence. By developing systems that not only address data anomalies but also seek to understand and correct them, we are setting a new standard for data quality and insight generation in the industry.

"Manolin’s development of modules like this shows its dedication to maximizing the quality and value of its data," Costantino said. “Messy data doesn’t always mean unusable data. The phrase 'garbage in, garbage out' is often used by those who overlook the potential within imperfect datasets. Even the worst data can reveal valuable insights. Modules like the one built by Jonah highlights our commitment to extracting as much insight as possible from aquaculture data.”

As we continue to push the boundaries of what’s possible in aquaculture data intelligence, we are excited to see how innovations like the self-healing data module will shape the future of our platform and the industry as a whole.