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Скачать или смотреть SRE for ML Infra // Todd Underwood // MLOps Coffee Sessions #23

  • MLOps.community
  • 2020-12-22
  • 1311
SRE for ML Infra // Todd Underwood // MLOps Coffee Sessions #23
todd underwood pittsburghtodd underwood dpetodd underwood cbdusenix 2020machine learningSREml infrastructureml infrastructure engineerml infrastructure tools for productionsrecon 2020srecon youtubereconnectgooglegoogle engineergoogle engineering manager interviewgoogle machine learninggoogle machineuniversity of new mexico
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Описание к видео SRE for ML Infra // Todd Underwood // MLOps Coffee Sessions #23

Coffee Sessions #23 with Todd Underwood of Google, Followups from OPML Talks on ML Pipeline Reliability co-hosted by Vishnu Rachakonda.

//Bio
Todd is a Director at Google and leads Machine Learning for Site Reliability Engineering Director. He is also a Site Lead for Google’s Pittsburgh office. ML SRE teams build and scale internal and external ML services and are critical to almost every Product Area at Google.

Before working at Google, Todd held a variety of roles at Renesys. He was in charge of operations, security, and peering for Renesys’s Internet intelligence services that are now part of Oracle's Cloud service. He also did product work for some early social products that Renesys worked on. Before that Todd was Chief Technology Officer of Oso Grande, an independent Internet service provider (AS2901) in New Mexico.

-------------- ✌️Connect With Us ✌️ ------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, Feature Store, Machine Learning Monitoring and Blogs: https://mlops.community/

Connect with Demetrios on LinkedIn:   / dpbrinkm  
Connect with Vishnu on LinkedIn:   / vrachakonda  
Connect with Todd on LinkedIn:   / toddunder  

Timestamps:
[00:00] Intro to Todd Underwood
[02:04] Todd's background
[08:54] What's kind of vision do you "paint"?
[14:54] Playing a little bit "devil's advocate." Do you think that's even possible?
[19:36] "Start serving to make sure of having the possibility to get it out." How do you feel about that?
[23:56] What advice could you give to other people who wanted to bring ML professionals into their companies to make ML useful for them?
[29:53] Is it useful to use these new models?
[32:25] Do you feel like there would be a point where there would be a standard procedure?
[35:50] How machine learning breaks
[40:44] As an engineering leader, what's your advice to other engineering leaders in terms of how to make that reflection on your team's needs and failures...?
[48:42] It's the design that you're looking at as the problem, not the person.
[56:27] Do we think that people sold a bunch of stuff and now we were left with the results?
[1:00:46] Recommendations on readings, things to do to better hone our craft.
[1:03:35] The more you explore, the more you realize, what's going on? Where can I learn from?
[1:05:00] Since you are in the mode of predicting things and philosophical background, where are you seeing the industry going in the next 5 years as we create it?

Resources referenced in this episode:
   • SREcon19 Europe/Middle East/Africa - All o...  
   • OpML '20 - How ML Breaks: A Decade of Outa...  
   • How To Move From Barely Doing BI to Doing ...  
   • Monzo Bank - An MLOps Case Study // Neal L...  
https://www.usenix.org/conference/opm...
https://www.usenix.org/system/files/l...
https://storage.googleapis.com/pub-to...

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