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 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
