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Scale Zeitgeist: AI Readiness Report: ML & AI Meetup August 2022: San Francisco (on-demand)

Posted Sep 30, 2022 | Views 1.8K
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Andrew Kondrich
Machine Learning Research Engineer @ Scale AI

Andrew Kondrich is the tech lead for Scale Enterprise AI, the machine learning suite for Fortune 500 companies. Prior to joining Enterprise AI, Andrew built ML-assisted tooling for Scale's 2D annotation business, including Autosegment. Andrew received his M.S. and B.S. in Computer Science from Stanford University, where he did research in robotics and healthcare advised by Prof. Andrew Ng and Prof. Fei-Fei Li.

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Russell Kaplan
Director of Engineering @ Scale AI

Russell Kaplan leads Scale Nucleus, the data management platform for machine learning teams. He was previously founder and CEO of Helia AI, a computer vision startup for real-time video understanding, which Scale acquired in 2020. Before that, Russell was a senior machine learning scientist on Tesla's Autopilot team, and he received his M.S. and B.S. from Stanford University, where he was a researcher in the Stanford Vision Lab advised by Fei-Fei Li.

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SUMMARY

Scale Zeitgeist: AI Readiness Report Today, every industry has begun to understand the transformative potential of AI and invest in it across their organizations. However, the next question is: are those investments succeeding? And are the organizations set up in the right way to foster meaningful outcomes? Is your business truly “AI-ready?” The Scale Zeitgeist: AI Readiness Report, a survey of more than 1,300+ ML practitioners, explores where AI innovation is being bottlenecked, and what approaches are helping companies find success. The goal of the report is to empower organizations and ML practitioners to clear current hurdles, learn and implement best practices, and unlock the full potential of AI for every business. In this talk, Russell Kaplan, Director of Engineering at Scale AI, and Andrew Kondrich, Machine Learning Research Engineer at Scale AI, will discuss the top challenges faced by ML practitioners across data and annotation, to model development and deployment as well as the best practices to overcome these challenges.

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