Larysa Visengeriyeva is a software and machine learning engineer at INNOQ, where she works at the intersection of software engineering and machine learning. Her focus is MLOps: the discipline of building machine learning systems that are robust enough to leave the prototype stage and deliver reliable value in production. She brings an engineering-first mindset to a field where, by many estimates, a large majority of ML projects still fail to reach production.

Larysa holds a PhD in the field of Augmented Data Cleaning, research that connects data quality, human-in-the-loop workflows, and machine learning. That academic grounding shapes how she approaches applied work: clean, well-understood data comes first, and engineering discipline is what turns promising models into dependable software.

She is an active conference speaker and community contributor. At the DataTalks.Club conference she presented the ten foundational practices of machine learning engineering, distilling lessons on how teams can structure their projects to succeed. Through her speaking, writing, and open work around MLOps, Larysa helps practitioners close the gap between data science experiments and production-grade systems.