Rani Radhakrishnan, Principal at PwC, discusses the convergence of MLOps and IT operations, the practicalities of deploying AI agents, and the strategic considerations for scaling and sustaining AI initiatives in the enterprise. The conversation covers the shift from experimentation to ROI, the importance of human-in-the-loop processes, and the evolving skillsets required for future-ready IT.
Adobe's journey in building a sophisticated AI Compute Platform to tackle the immense challenges of GPU optimization for training large-scale generative models like Firefly. The talk covers their custom-built solutions for resource management, developer productivity, and automated fault tolerance.
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.