undefined

Shreya Shankar

ML Engineer with experience at Facebook and Google Brain; currently a PhD candidate at UC Berkeley, focusing on data management for AI/ML software.

Top 5 podcasts with Shreya Shankar

Ranked by the Snipd community
undefined
49 snips
Sep 30, 2024 • 48min

AI Agents for Data Analysis with Shreya Shankar - #703

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.
undefined
28 snips
Mar 3, 2023 • 55min

Shreya Shankar — Operationalizing Machine Learning

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​
undefined
20 snips
Jun 26, 2024 • 1h 30min

Episode 29: Lessons from a Year of Building with LLMs (Part 1)

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.
undefined
12 snips
Jul 27, 2024 • 1h 15min

Episode 32: Building Reliable and Robust ML/AI Pipelines

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.
undefined
6 snips
Sep 7, 2023 • 1h 17min

Shreya Shankar: Machine Learning in the Real World

In this episode, Shreya Shankar, a computer scientist pursuing her PhD in databases at UC Berkeley, discusses the challenges faced by machine learning engineers, including ensuring consistent performance and accuracy in production ML models. She also explores the importance of preserving information for anomaly detection, quantitative statistics for data cleaning, and the evolution from MLOps to LLMOps and FMOps. The speakers discuss the difficulties of implementing ML models and ML monitoring, as well as varying perspectives on AI as a product and its impact on customer expectations.