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The Future of Search in the Era of Large Language Models // Saahil Jain // MLOps Podcast #150
Description
MLOps Coffee Sessions #150 with Saahil Jain, The Future of Search in the Era of Large Language Models, co-hosted by David Aponte.
// Abstract
Saahil shares insights into the You.com search engine approach, which includes a focus on a user-friendly interface, third-party apps, and the combination of natural language processing and traditional information retrieval techniques. Saahil highlights the importance of product thinking and the trade-offs between relevance, throughput, and latency when working with large language models.
Saahil also discusses the intersection of traditional information retrieval and generative models and the trade-offs in the type of outputs they produce. He suggests occupying users' attention during long wait times and the importance of considering how users engage with websites beyond just performance.
// Bio
Saahil Jain is an engineer at You.com. At You.com, Saahil builds searching and ranking systems. Previously, Saahil was a graduate researcher in the Stanford Machine Learning Group under Professor Andrew Ng, where he researched topics related to deep learning and natural language processing (NLP) in resource-constrained domains like healthcare. His research work has been published in machine learning conferences such as EMNLP, NeurIPS Datasets & Benchmarks, and ACM-CHIL, among others. He has publicly released various machine learning models, methods, and datasets, which have been used by researchers in both academic institutions and hospitals across the world, as part of an open-source movement to democratize AI research in medicine. Prior to Stanford, Saahil worked as a product manager at Microsoft on Office 365. He received his B.S. and M.S. in Computer Science from Columbia University and Stanford University, respectively.
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// Related Links
Website: http://saahiljain.me/
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Timestamps
[00:00] Saahil's preferred coffee
[04:32] Saahil Jain's background
[04:44] Takeaways
[07:49] Search Landscape
[12:57] Use cases exploration
[14:51] Differentiating what to give to users
[17:19] Search key challenges
[20:05] Search objective relevance
[23:22] MLOps Search and Recommender Systems
[26:54] Addressing Latency Issues
[29:41