From January 26 to 30, 2026, ML Week 2026 took place in the city of Ostroh at the National University of Ostroh Academy – one of the oldest universities in Europe. This is the second year that ML Week has served as a platform for collaboration between universities and IT companies on real machine learning challenges. The event format is a five-day intensive, during which students work on engineering ML tasks derived from real business practice, supported by industry mentors.
This is the second year that ML Week has served as a platform for collaboration between universities and IT companies on real machine learning challenges. The event format is a five-day intensive, during which students work on engineering ML tasks derived from real business practice, supported by industry mentors.
When and where it took place
ML Week 2026 was held on January 26–30 in Ostroh – a city with a deep educational history. Hosting the event at the Ostroh Academy symbolically combined the traditions of academic education with modern technological fields, particularly Machine Learning and Data Science.
Who participated this year
This year, around 70 students from 6 Ukrainian universities joined ML Week:
- National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute
- Lviv Polytechnic National University
- National Technical University Kharkiv Polytechnic Institute
- National University of Ostroh Academy
- Dnipro University of Technology
- Ivan Franko National University of Lviv
Students were grouped into 23 inter-university teams, where representatives from different universities collaborated on shared tasks.
What the teams worked on
Partner companies proposed 10 open ML challenges taken directly from their day-to-day operations. Over four days, teams worked on solutions, and on the final day presented working prototypes.
As part of ML Week 2026, students worked on the following projects:
- Water Turbidity Level Detection (EPAM)
- Edge-Optimized Quality Inspection System (GlobalLogic)
- Molecular Toxicity Prediction (Enamine)
- Music Plagiarism Detection (IT-Jim)
- Recipe Recommendation System (Crux Lab)
- SLM Translator (Squad)
- Plant Diseases Classification (NIX)
- Campaign Insight Studio (Simulmedia)
- Intra-Pod Confidence Studio (Simulmedia)
- GNSS Reliability Studio (Simulmedia)
The tasks covered Data Science and Computer Vision and reflected real-world challenges faced by ML teams in the industry
Why this format works
A key feature of ML Week is that multiple independent teams work on the same project simultaneously. This allows comparison of different approaches to solving a single problem and results in multiple alternative prototypes.
Throughout the week, teams worked closely with mentors from IT companies, who helped them quickly understand the context of the tasks and focus on practical outcomes. Special attention this year was given to junior students — companies prepared tasks with adjusted complexity while maintaining real engineering value.
ML Week 2026 once again demonstrated that collaboration between universities and IT businesses on real-world problems creates a valuable educational experience for future ML engineers.
We thank all universities, mentors, partners, and students for their collaboration this year.
We look forward to continuing ML Week next year.