The 2nd BDA hackathon on smart technologies has concluded

Saturday, April 18, was a busy and exciting day, as the BDA AI School’s second program – “Smart Technologies and Generative AI” – hosted its hackathon. Throughout the day, participants worked diligently on developing new ideas.

As part of the hackathon, teams were tasked with creating various machine learning solutions based on open datasets related to Latvia’s state budget estimates.

Participants immersed themselves in exploring what could be achieved with the given dataset. By applying the knowledge and skills gained during the BDA AI School’s second program, they used clustering and classification algorithms to identify anomalies in public data, uncover examples of inefficient resource allocation, and recognize opportunities to optimize budget planning processes. This type of data processing is essential for making information accessible and understandable to a broader audience, enabling students, journalists, and anyone interested to use it effectively.

As the participants themselves noted, this hackathon was a valuable experience that demonstrated the immense potential of data analytics and machine learning solutions in optimizing public administration processes and saving time.

At the end of the hackathon, teams presented their ideas. Each team was evaluated by both BDA AI School mentors and fellow participants.

The award for best execution was given to team “Pētnieki neziņā,” represented by Arta Putniņa, Viktorija Golubeva, and Mārtiņš Brezauckis.

Meanwhile, the most successful idea was awarded to team “Izmeklētāji” – Raitis Mačiņš, Igors Lukjanovs, and Viktors Veliks, who explored the accessibility of pharmacy services for residents of Latvia.

The teams were supported and guided by experienced industry professionals – BDA AI School mentors and program content developers – Toms Bergmanis, Mārcis Pinnis, and Martins Kronis.

Mārcis Pinnis: “Hackathon participants actively leveraged AI capabilities to solve problems, better understand the data, and select the most appropriate machine learning methods to bring their ideas to life.”