Agentic AI goes beyond rule-based automation, learning and adapting to dynamic tasks. Unlike traditional automation, it acts autonomously, handles exceptions, and drives smarter, scalable enterprise outcomes.
Many AI projects stall after PoC due to data complexity, scalability issues, lack of monitoring, and missing governance. Ignatiuz AI CoE guides enterprises to scale AI successfully from concept to production.
Learn how to prepare your data for AI success with structured audits, cleaning, indexing, harmonization, security, and continuous improvement to ensure reliable outcomes for LLM and Agentic AI initiatives.
Learn how to transform a GenAI prototype into a production ready system with scalable structure, practices, essential files, and workflows that simplify collaboration, deployment, and long term maintainability.
Learn how to train YOLO models efficiently with best practices for dataset preparation, model selection, hyperparameter tuning, infrastructure choices, and common pitfalls to avoid for accurate object detection.
Learn why custom trained computer vision models outperform generic AI, and how precise data annotation, proper labeling strategies, and quality control directly impact accuracy, reliability, and real world AI vision performance.