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How to Optimize DAPT for Serverless Computing
DAPT is an open source, cloud-based ML tool. It's designed to be serverless so it can run in Python on a laptop and then spin down as soon as the work is done. The team behind DAPT has backgrounds in tiny ML; they're looking at ways to make that even more possible.
MLOps Coffee Sessions #165 with Sammy Sidhu, Python Power: How Daft Embeds Models and Revolutionizes Data Processing. // Abstract Sammy shares his fascinating journey in the autonomous vehicle industry, highlighting his involvement in two successful startup acquisitions by Tesla and Toyota. He emphasizes his expertise in optimizing and distilling models for efficient machine learning, which he has incorporated into his new company Eventual. The company's open-source offering, daf, focuses on tackling the challenges of unstructured and complex data. Sammy discusses the future of MLOps, machine learning, and data storage, particularly in relation to the retrieval and processing of unstructured data. The Eventual team is developing Daft, an open-source query engine that aims to provide efficient data storage solutions for unstructured data, offering features like governance, schema evolution, and time travel. The conversation sheds light on the innovative developments in the field and the potential impact on various industries. // Bio Sammy is a Deep Learning and systems veteran, holding over a dozen publications and patents in the space. Sammy graduated from the University of California, Berkeley where he did research in Deep Learning and High Performance Computing. He then joined DeepScale as the Chief Architect and led the development of perception technologies for autonomous vehicles. During this time, DeepScale grew rapidly and was subsequently acquired by Tesla in 2019. Staying in Autonomous Vehicles, Sammy joined Lyft Level 5 as a Senior Staff Software Engineer, building out core perception algorithms as well as infrastructure for machine learning and embedded systems. Level 5 was then acquired by Toyota in 2021, adopting much of his work. Sammy is now CEO and Co-Founder at Eventual Building Daft, an open-source query engine that specializes in multimodal data. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://sammysidhu.com/ Check out Daft, our open-source query engine for multimodal data! https://www.getdaft.io/ Here are some talks/shows we have given about it: - PyData Global (Dec 2022): Large-scale image processing: https://www.youtube.com/watch?v=ol6IQUbyeDo&ab_channel=PyData - Ray Meetup (March 2023): Distributed ML preprocessing + training on Ray https://www.youtube.com/watch?v=1MpEYlIlu7w&t=2972s&ab_channel=Anyscale - The Data Stack Show (April 2023): Self-Driving Technology and Data Infrastructure with Sammy Sidhu https://datastackshow.com/podcast/the-prql-self-driving-technology-and-data-infrastructure-with-sammy-sidhu-co-founder-and-ceo-of-eventual/ Chain of Thought for LLMs: https://cobusgreyling.medium.com/chain-of-thought-prompting-in-llms-1077164edf97 Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes: https://arxiv.org/abs/2305.02301 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sammy on LinkedIn: https://www.linkedin.com/in/sammy-sidhu/
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