Working across both data science and ML engineering gives him a particularly complete view of how data quality, modeling choices, evaluation, and infrastructure constrain and reinforce one another when implementing industrial AI.

And his academic background gives that breadth of roles a clear foundation.

Artem holds a Bachelor’s degree in Physics and a Master’s degree in Mathematics and Control Methods, a foundation he has since carried into work across forecasting, NLP, recommendation systems, computer vision, and LLMs.

He continues to build on that base through advanced professional training, including OTUS’s Advanced Machine Learning program.

Manufacturing is where many of those strands come together and Artem’s analytical approach becomes particularly tangible.

At Aristek, Artem leads computer vision work for quality control in automobile production, combining data science with the engineering and technical coordination behind the full model lifecycle.

Artem’s low tolerance for small inaccuracies shows up in the way he improves the process around the models, caring about narrowing even the smallest gap between projected metrics and real-world use.

He introduced version control into training and data pipelines, reduced the number of manual steps required whenever new models are released, and made the entire workflow easier to trace, maintain, and evolve over time.

His writing, including articles published on Medium, grows directly out of his project work. Artem examines the less visible decisions that start to matter as a project matures and how even minor choices can reshape a team’s day-to-day work.