// 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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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