To build secure AI systems, we must first learn to break them. Katharine Jarmul explores the landscape of adversarial AI, detailing how attackers exploit fundamental weaknesses in deep learning models—from poisoned training data and overparameterization to the attention mechanism itself. This talk provides a practical taxonomy of attacks and a primer on building robust defenses.
Simba Khadder explains the evolution of feature stores and MLOps, detailing why they remain crucial in the age of LLMs for high-scale use cases. He discusses the acquisition of his company, Featureform, by Redis and outlines their new vision: building a "Context Engine" for AI. This engine aims to unify structured data, unstructured data, and memory into a single pane of glass, moving beyond simple RAG to a more sophisticated "Context Engineering 2.0" that empowers agents with rich, queryable context.
Explore the concept of flexible workload orchestration as a unified solution to manage diverse application types, from traditional web services to complex AI/ML pipelines. This approach simplifies operations, breaks down tooling silos, and provides a future-proof infrastructure for evolving AI technologies.
Andy Pernsteiner, Field CTO of VAST Data, discusses the immense challenges of transitioning AI projects from prototype to production. He highlights the critical role of data infrastructure, the high cost of GPU downtime, and the necessity of building resilient, scalable platforms that can withstand real-world failures like power outages in massive data centers. The conversation emphasizes a shift in mindset towards empathy, better requirement gathering, and closer collaboration between data scientists and platform engineers to bridge the gap between development and operations.
Databricks’ Eric Peter and Corey Zumar introduce MLflow 3.0, focusing on its new "Agentic Insights" capabilities. They demonstrate how MLflow is evolving from providing tools for manual quality assurance in Generative AI to using intelligent agents to automatically find, diagnose, and prioritize issues, significantly speeding up the development lifecycle.
Alex Salazar, CEO of Arcade, argues that the true value of AI is not in chatbots but in agents that can take real-world actions. He details the primary reasons agents fail to reach production—security, cost, latency, and accuracy—and introduces an "Agent Hierarchy of Needs" as a framework for building robust, production-ready agents. The talk emphasizes a critical shift from exposing raw APIs to building intention-based tools and solving the complex challenge of agent authorization through a delegated model.