07-29, 10:45–11:15 (US/Eastern), Filmhouse
Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
In the world of AI, personalization isn’t just nice to have — it's a must-have. Mike Del Balso, CEO and Co-Founder of Tecton, presents "Full RAG," an architectural approach that enhances AI recommendation systems by incorporating comprehensive contextual insights and real-time user data. This approach, rooted in the Retrieval-Augmented Generation (RAG) architecture, aims to transcend traditional models' limitations by integrating batch and streaming data, enabling a deeper layer of personalization.
The talk emphasizes the potential of Full RAG but also addresses the technical challenges in implementing such a system, including data management and model training complexities. Del Balso introduces Tecton's feature platform as a critical solution for navigating these obstacles, facilitating the seamless integration of diverse data types to power sophisticated AI applications. Attendees will leave with a clear understanding of the importance of hyperpersonalization in AI, insights into the Full RAG architecture's capabilities for enhancing personalization, and strategies for managing the complex data integrations required for deploying advanced AI solutions effectively.
Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto.