Shreya Shankar

Join Willem Pienaar, CTO of Cleric and creator of Feast, along with PhD student Shreya Shankar, as they tackle the toughest challenges in building AI agents. They discuss the ambiguity of 'ground truth' in evaluations, revealing three key gulfs of human-AI interaction that hinder success. The duo emphasizes the importance of moving humans out of the feedback loop, using implicit signals for faster learning. Practical techniques like heat maps for task failures and the complexities of simulated environments are also explored, shedding light on the inevitable performance ceiling of AI.

Shreya Shankar, a PhD student at UC Berkeley specializing in intelligent data processing, shares her insights on the innovative DocETL system. They discuss how this technology optimizes LLM-powered data pipelines, enhancing analysis of complex documents. Shreya highlights the challenges of data extraction from PDFs, the importance of human feedback in AI systems, and the need for tailored benchmarks in data processing. Real-world applications and the future of agentic systems are also examined, showcasing a visionary path in data management.

About This EpisodeShreya Shankar is a computer scientist, PhD student in databases at UC Berkeley, and co-author of "Operationalizing Machine Learning: An Interview Study", an ethnographic interview study with 18 machine learning engineers across a variety of industries on their experience deploying and maintaining ML pipelines in production.Shreya explains the high-level findings of "Operationalizing Machine Learning"; variables that indicate a successful deployment (velocity, validation, and versioning), common pain points, and a grouping of the MLOps tool stack into four layers. Shreya and Lukas also discuss examples of data challenges in production, Jupyter Notebooks, and reproducibility.Show notes (transcript and links): http://wandb.me/gd-shreya---💬 *Host:* Lukas Biewald---*Subscribe and listen to Gradient Dissent today!*👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Experts from Amazon, Hex, Modal, Parlance Labs, and UC Berkeley share lessons learned from working with Large Language Models. They discuss the importance of evaluation and monitoring in LLM applications, data literacy in AI, the fine-tuning dilemma, real-world insights, and the evolving roles of data scientists and AI engineers.

Join Shreya Shankar, a UC Berkeley researcher specializing in human-centered data management systems, as she navigates the exciting world of large language models (LLMs). Discover her insights on the shift from traditional machine learning to LLMs and the importance of data quality over algorithm issues. Shreya shares her innovative SPaDE framework for improving AI evaluations and emphasizes the need for human oversight in AI development. Plus, explore the future of low-code tools and the fascinating concept of 'Habsburg AI' in recursive processes.