Uma Ramadoss from AWS explains the core concepts of Agentic AI, differentiating it from standard AI workflows. The session covers how to build agentic systems on AWS using services like Bedrock and Step Functions, and open-source frameworks like Strands SDK, emphasizing practical architecture, context enrichment, and the importance of verification.
David Jayatillake, VP of AI at Cube.dev, discusses the critical role of a headless, open-source semantic layer in the modern data stack. He argues against proprietary, BI-tool-specific semantic layers that create vendor lock-in and advocates for a decoupled approach. The conversation explores how AI agents can automate the entire data pipeline—from ingestion and transformation to generating and querying the semantic layer—and compares the functionalities of semantic layers and feature stores, highlighting the crucial difference of temporality.
Explore the shift from reactive to predictive DevSecOps with Akshay Mittal. This discussion covers how AI-Augmented DevSecOps and Agentic Workflows are creating self-healing systems, the critical role of Explainable AI (XAI), and a four-layer architecture for building scalable, enterprise-grade AI solutions.
A guide to understanding the differences between Large Language Models (LLMs), Small Language Models (SLMs), and Frontier Models (FMs). Learn the unique strengths of each model type and see practical use cases for document classification, customer support, and incident response to help you choose the right model for your AI project.
Akshay Mittal discusses the evolution of enterprise AI, focusing on the crucial shift from reactive to predictive security through AI-augmented DevSecOps. He explores how to productionize agentic AI workflows using AIOps and Kubernetes, and emphasizes the non-negotiable need for explainable AI (XAI) in critical systems.
Mem0 is building a model-neutral, persistent memory layer for AI agents to solve the fundamental statelessness of LLMs. Co-founders Taranjeet Singh and Deshraj Yadav discuss their hybrid memory architecture, which reduces cost and latency compared to context stuffing, and their vision for a future where user memory is portable across all AI applications.