Larysa

Larysa V. is a PhD-qualified expert in data quality and machine learning operations. This background sharpens her perspective on systemic interrelationships.
Larysa brings solid practical experience in the data and AI domain and operates at the intersection of technology, organization, and productive implementation.
Her strength lies in structuring requirements clearly, transparently clarifying expectations, and ensuring results through clear communication and reliable processes- a key success factor in data-driven and organizational change processes.
In her current work, she applies these competencies to topics such as data and AI strategies, quality requirements in machine learning systems, and organization-related development processes in the digital context.

Larysa believes that technology should support people, not replace them. Data and AI are tools for value creation and responsible problem-solving.
She emphasizes quality, transparency, and traceability to ensure technical systems function reliably and decisions remain understandable.
From her perspective, learning and development are central drivers of change. Structure and clarity provide the framework in which new competencies can emerge.
Innovation should be pragmatic and responsible. Progress is important, but it must consider ethical and organizational consequences and be aimed at sustainable impact.

Development of a widely used knowledge platform that makes machine learning operations accessible to companies and professionals, significantly contributing to the professionalization of AI projects.
Author of “The AI Engineer’s Guide to Surviving the EU AI Act,” a practical book that explains regulatory requirements clearly and provides concrete implementation guidance for AI projects.
Through her work at the interface of science and practice, she has translated complex AI and data strategies into application-oriented solutions, helping organizations implement data-driven innovations sustainably.
Her commitment to responsible AI and quality-focused data strategies shapes the discussion on modern machine learning systems and makes her a sought-after voice in the professional community.

Larysa presents content in a practical and understandable way, with a clear focus on learning outcomes and applicability.
Her style is open and dialogue-oriented, without losing sight of the learning process. Questions and interaction are explicitly part of Larysa’s approach.
Trainings are designed to be hands-on and transferable, enabling participants to apply knowledge directly in their professional work.