An in-depth guide to DSPy, a framework for programming with language models, not just prompting them. Learn its core concepts—Signatures, Modules, Adapters, and Optimizers—and see real-world examples of building robust, testable, and transferable AI applications for the enterprise.
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.
An expert overview of post-training techniques for language models, covering the entire workflow from data generation and curation to advanced algorithms like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning (RL), along with practical advice on evaluation and iteration.
A deep dive into orchestrating swarms of AI agents for large-scale code refactoring. Learn practical workflows, task decomposition strategies, and context-sharing patterns, demonstrated with a real-world case study on migrating a large codebase.
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.