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Racing the Playhead: Real-time Model Inference in a Video Streaming Environment // Brannon Dorsey // Coffee Sessions #98

Racing the Playhead: Real-time Model Inference in a Video Streaming Environment // Brannon Dorsey // Coffee Sessions #98

FromMLOps.community


Racing the Playhead: Real-time Model Inference in a Video Streaming Environment // Brannon Dorsey // Coffee Sessions #98

FromMLOps.community

ratings:
Length:
58 minutes
Released:
May 12, 2022
Format:
Podcast episode

Description

MLOps Coffee Sessions #98 with Brannon Dorsey, Racing the Playhead: Real-time Model Inference in a Video Streaming Environment co-hosted by Vishnu Rachakonda.

// Abstract
Runway ML is doing an incredibly cool workaround applying machine learning to video editing. Brannon is a software engineer there and he’s here to tell us all about machine learning in video and how Runway maintains their machine learning infrastructure.

// Bio
Brannon Dorsey is an early employee at Runway, where he leads the Backend team. His team keeps infrastructure and high-performance models running at scale and helps to enable a quick iteration cycle between the research and product teams.

Before joining Runway, Brannon worked on the Security Team at Linode. Brannon is also a practicing artist who uses software to explore ideas of digital literacy, agency, and complex systems.

// MLOps Jobs board  
https://mlops.pallet.xyz/jobs

// Related Links
Website: https://brannon.online
Blog: https://runwayml.com/blog/distributing-work-adventures-queuing-and-autoscaling/

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Brannon on LinkedIn: https://www.linkedin.com/in/brannon-dorsey-79b0498a/

Timestamps:
[00:00] Introduction to Brannon Dorsey
[00:56] Takeaways
[05:42] Runway ML
[07:00] Replacement for Imovie?
[09:07] Machine Learning use cases of Runway ML
[10:40] Journey of starting as a model zoo to video editor
[14:42] Rotoscoping  
[16:23] Intensity of ML models in Runway ML and engineering challenges
[19:55] Deriving requirements
[23:10] Runway's model perspective
[25:25] Why browser hosting?
[27:19] Abstracting away hardware
[32:04] Kubernetes is your friend
[35:29] Statelessness is your friend
[38:17] Merge to master quickly
[42:57] Brannon's winding history becoming an engineer
[46:49] How much do you use Runway?
[49:37] Last book read
[50:36] Last bug smashed
[52:21] MLOps marketing that made eyes rolling
[54:11] Bullish on technology that might surprise people
[54:39] Spot by netapp
[56:42] Implementing Spot by netapp
[56:55] How do you want to be remembered?
[57:22] Wrap up
Released:
May 12, 2022
Format:
Podcast episode

Titles in the series (100)

Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.