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MLOps Investments

Posted Apr 06
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SPEAKER
Sarah Catanzaro
Sarah Catanzaro
Sarah Catanzaro
Partner @ Amplify Partners

Sarah Catanzaro is a Partner at Amplify Partners, where she focuses on investing in and advising high potential startups in machine intelligence, data management, and distributed systems. Her investments at Amplify include startups like RunwayML, Maze Design, OctoML, and Metaphor Data among others. Sarah also has several years of experience defining data strategy and leading data science teams at startups and in the defense/intelligence sector including through roles at Mattermark, Palantir, Cyveillance, and the Center for Advanced Defense Studies.

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Sarah Catanzaro is a Partner at Amplify Partners, where she focuses on investing in and advising high potential startups in machine intelligence, data management, and distributed systems. Her investments at Amplify include startups like RunwayML, Maze Design, OctoML, and Metaphor Data among others. Sarah also has several years of experience defining data strategy and leading data science teams at startups and in the defense/intelligence sector including through roles at Mattermark, Palantir, Cyveillance, and the Center for Advanced Defense Studies.

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SUMMARY

Many have discussed the role of VC in the MLOps ecosystem. In this Coffee Session, we chatted with Sarah Catanzaro, an investor at Amplify Partners, who gave us her take on trends in MLOps. Sarah gave us insight into how a former head of data turns her experience into investments. It's truly a small world--Sarah invested in the company run by last week's meetup guest Josh, Flywheel ML! We had a wide-ranging discussion with Sarah, three takeaways stood out: 1. The relationship between unstructured data and structured data is due for change. In most settings, you have some form of structured data (i.e. a metadata table) and unstructured data (i.e. images, text, etc.) Managing the relationship between these forms of data can constitute the bulk of MLOps. Because of this difficulty, Sarah forecasted new tooling arising to make data management easier. 2. Academic benchmarks suffer from a lack of transparency on production/industry use cases. In conversation with Andrew Ng, Sarah shared her lesson that despite all the blame industry professionals place on academics for narrowly optimizing to benchmarks with little practical meaning, they also share the blame for making it difficult to create meaningful benchmarks. Companies are loath to share realistic data and the true context in which ML has to operate. 3. MLOps is due for consolidation, especially as companies adopt platform-driven strategies. As many of you all know, there are tons and tons of MLOps tools out there. As more companies address these challenges, Sarah predicted that many of the point solutions would start to be consolidated into larger platforms.

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