Data to Decisions: Modern Architectures in AI Systems

Session details:

Artificial Intelligence systems are evolving rapidly, driven by advances in data infrastructure, model architectures, and deployment strategies. This talk explores the end-to-end journey from raw data to intelligent decision-making in modern AI systems. We’ll dive into key components such as data pipelines, feature engineering, model training at scale, and the integration of deep learning and transformer-based models. Emphasis will be placed on the architectural patterns that support real-time inference, feedback loops for continuous learning, and hybrid AI systems that combine symbolic reasoning with machine learning. We will also address the challenges of building scalable, robust, and ethical AI systems in production environments. Attendees will gain a clear understanding of how to architect AI solutions that are not only accurate but also efficient, interpretable, and aligned with real-world use cases. Whether you're designing AI for business, research, or social good, this session will provide insights to turn data into impactful decisions.