Chris Mattmann — ML Applications on Earth, Mars, and Beyond

Chris Mattmann — ML Applications on Earth, Mars, and Beyond

Chris shares some of the incredible work and innovations behind deep space exploration at NASA JPL and reflects on the past, present, and future of machine learning.

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Chris Mattmann is the Chief Technology and Innovation Officer at NASA Jet Propulsion Laboratory, where he focuses on organizational innovation through technology. He's worked on space missions such as the Orbiting Carbon Observatory 2 and Soil Moisture Active Passive satellites.

Chris is also a co-creator of Apache Tika, a content detection and analysis framework that was one of the key technologies used to uncover the Panama Papers, and is the author of "Machine Learning with TensorFlow, Second Edition" and "Tika in Action".


Connect with Chris:

Personal website: https://www.mattmann.ai/

Twitter: https://twitter.com/chrismattmann


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Topics Discussed:

0:00 Sneak peek, intro

0:52 On Perseverance and Ingenuity

8:40 Machine learning applications at NASA JPL

11:51 Innovation in scientific instruments and data formats

18:26 Data processing levels: Level 1 vs Level 2 vs Level 3

22:20 Competitive data processing

27:38 Kerbal Space Program

30:19 The ideas behind "Machine Learning with Tensorflow, Second Edition"

35:37 The future of MLOps and AutoML

38:51 Machine learning at the edge


Transcript:

http://wandb.me/gd-chris-mattmann


Links Discussed:

Perseverance and Ingenuity: https://mars.nasa.gov/mars2020/

Data processing levels at NASA: https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy/data-levels

OCO-2: https://www.jpl.nasa.gov/missions/orbiting-carbon-observatory-2-oco-2

"Machine Learning with TensorFlow, Second Edition" (2020): https://www.manning.com/books/machine-learning-with-tensorflow-second-edition

"Tika in Action" (2011): https://www.manning.com/books/tika-in-action


Transcript:

http://wandb.me/gd-chris-mattmann


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Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:

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