The Emergence of Corporate Research Labs in AI

Google, Facebook and others have created large-scale ML models. But they're less accessible to everyone else than traditional research labs. Could be a new way of doing the stats that is cheaper? "I would say it's not crazy to imagine a world with much smaller models," says Fei-Fei Li.

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MLOps Coffee Sessions #155 with Matei Zaharia, The Birth and Growth of Spark: An Open Source Success Story, co-hosted by Vishnu Rachakonda.

// Abstract

We dive deep into the creation of Spark, with the creator himself, Matei Zaharia, Chief Technologist at Databricks. This episode also explores the development of Databricks' other open source home run, ML Flow, and the concept of "lake house ML". As a special treat, Matei talked to us about the details of the "DSP" (Demonstrate Search Predict) project, which aims to enable building applications by combining LLMs and other text-returning systems.

// About the guest:

Matei has the unique advantage of being able to see different perspectives, having worked in both academia and the industry. He listens carefully to people's challenges and excitement about ML and uses this to come up with new ideas. As a member of Databricks, Matei also has the advantage of applying ML to Databricks' own internal practices. He is constantly asking the question, "What's a better way to do this?"

// Bio

Matei Zaharia is an Associate Professor of Computer Science at Stanford and Chief Technologist at Databricks. He started the Apache Spark project during his Ph.D. at UC Berkeley, and co-developed other widely used open-source projects, including MLflow and Delta Lake, at Databricks. At Stanford, he works on distributed systems, NLP, and information retrieval, building programming models that can combine language models and external services to perform complex tasks. Matei’s research work was recognized through the 2014 ACM Doctoral Dissertation Award for the best Ph.D. dissertation in computer science, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE).

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// Related Links

https://cs.stanford.edu/~matei/https://spark.apache.org/

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