Geospatial Machine Learning at AWS with Kumar Chellapilla - #607

Geospatial Machine Learning at AWS with Kumar Chellapilla - #607

Today we continue our re:Invent 2022 series joined by Kumar Chellapilla, a general manager of ML and AI Services at AWS. We had the opportunity to speak with Kumar after announcing their recent addition of geospatial data to the SageMaker Platform. In our conversation, we explore Kumar’s role as the GM for a diverse array of SageMaker services, what has changed in the geospatial data landscape over the last 10 years, and why Amazon decided now was the right time to invest in geospatial data. We discuss the challenges of accessing and working with this data and the pain points they’re trying to solve. Finally, Kumar walks us through a few customer use cases, describes how this addition will make users more effective than they currently are, and shares his thoughts on the future of this space over the next 2-5 years, including the potential intersection of geospatial data and stable diffusion/generative models. The complete show notes for this episode can be found at twimlai.com/go/607

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Computer Vision for Cozmo, the Cutest Toy Robot Everrrrr! with Andrew Stein - TWiML Talk #102

Computer Vision for Cozmo, the Cutest Toy Robot Everrrrr! with Andrew Stein - TWiML Talk #102

In this episode, I'm joined by Andrew Stein, computer vision engineer at consumer robotics company Anki, and his partner in crime Cozmo, a toy robot with tons of personality. Andrew joined me during t...

30 Jan 201843min

Expectation Maximization, Gaussian Mixtures & Belief Propagation, OH MY! w/ Inmar Givoni - Talk #101

Expectation Maximization, Gaussian Mixtures & Belief Propagation, OH MY! w/ Inmar Givoni - Talk #101

In this episode i'm joined by Inmar Givoni, Autonomy Engineering Manager at Uber ATG, to discuss her work on the paper Min-Max Propagation, which was presented at NIPS last month in Long Beach. Inmar ...

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A Linear-Time Kernel Goodness-of-Fit Test - NIPS Best Paper '17 - TWiML Talk #100

A Linear-Time Kernel Goodness-of-Fit Test - NIPS Best Paper '17 - TWiML Talk #100

In this episode, I speak with Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo and Kenji Fukumizu, who, alongside Wenkai Xu authored the 2017 NIPS Best Paper Award winner “A Linear-Time Kernel Goodne...

24 Jan 201822min

Solving Imperfect-Information Games with Tuomas Sandholm - NIPS ’17 Best Paper - TWiML Talk #99

Solving Imperfect-Information Games with Tuomas Sandholm - NIPS ’17 Best Paper - TWiML Talk #99

In this episode I speak with Tuomas Sandholm, Carnegie Mellon University Professor and Founder and CEO of startups Optimized Markets and Strategic Machine. Tuomas, along with his PhD student Noam Brow...

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Separating Vocals in Recorded Music at Spotify with Eric Humphrey - TWiML Talk #98

Separating Vocals in Recorded Music at Spotify with Eric Humphrey - TWiML Talk #98

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Accelerating Deep Learning with Mixed Precision Arithmetic with Greg Diamos - TWiML Talk #97

Accelerating Deep Learning with Mixed Precision Arithmetic with Greg Diamos - TWiML Talk #97

In this show I speak with Greg Diamos, senior computer systems researcher at Baidu. Greg joined me before his talk at the Deep Learning Summit, where he spoke on “The Next Generation of AI Chips.” Gre...

17 Jan 201839min

Composing Graphical Models With Neural Networks with David Duvenaud - TWiML Talk #96

Composing Graphical Models With Neural Networks with David Duvenaud - TWiML Talk #96

In this episode, we hear from David Duvenaud, assistant professor in the Computer Science and Statistics departments at the University of Toronto. David joined me after his talk at the Deep Learning S...

15 Jan 201835min

Embedded Deep Learning at Deep Vision with Siddha Ganju - TWiML Talk #95

Embedded Deep Learning at Deep Vision with Siddha Ganju - TWiML Talk #95

In this episode we hear from Siddha Ganju, data scientist at computer vision startup Deep Vision. Siddha joined me at the AI Conference a while back to chat about the challenges of developing deep lea...

12 Jan 201834min

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