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محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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Human-centric ML Infrastructure: A Netflix Original // Savin Goyal // MLOps Meetup #44

56:22
 
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Manage episode 313294488 series 3241972
محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

MLOps community meetup #44! Last Wednesday, we talked to Savin Goyal, Tech Lead for the ML Infra team at Netflix.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠

// Abstract:

In this conversation, Savin talked about some of the challenges encountered and choices made by the Netflix ML Infrastructure team while developing tooling for data scientists.

// Bio:

Savin is an engineer on the ML Infrastructure team at Netflix. He focuses on building generalizable infrastructure to accelerate the impact of data science at Netflix.

// Other links to check on Savin:
https://www.usenix.org/conference/opml20/presentation/cepoi
https://www.youtube.com/watch?v=lakPlz8GJcA&ab_channel=RConsortium
https://www.youtube.com/watch?v=-oMZAS9qfrE&ab_channel=AnalyticsIndiaMagazine
https://www.youtube.com/watch?v=yyWirT279tY&ab_channel=FunctionalTV
https://www.youtube.com/watch?v=QkRJ24Q0E-k&ab_channel=Matroid

----------- 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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Savin on LinkedIn: https://www.linkedin.com/in/savingoyal/

Timestamps:
[00:00] Background of Savin Goyal
[02:41] Breakdown of Metaflow
[05:44] In the stack, where does Metaflow stand?
[13:23] Where does Metaflow start in the Runway Project?
[15:27] What tools or storage does Netflix use for DataOps, ie, the front-end management of data sets, and how does that integrate with Metaflow? [18:56] Recommender Systems: Can you explain the other areas that you're using Machine Learning in?
[22:27] What do you feel is the hardest part of building an operating Machine Learning workflow? [28:45] 3 Pillars: Reproducibility, Scalability, Usability.
[36:05] You give so much power to people. How do you keep them from going overboard?
[37:47] Can you explain this Pillar of Usability?
[41:09] Road-based access control has been coming up a lot recently. Does Metaflow do something specific for that?
[44:49] What are some learnings that come across that you didn't have since you open-sourced when you were working at Netflix?
[48:10] What kind of trends have you been seeing? Where do you feel like the market is going?
[50:33] Have you seen some companies really interested in Metaflow? How have you been seeing them combine other tools that are out there?

  continue reading

482 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 313294488 series 3241972
محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

MLOps community meetup #44! Last Wednesday, we talked to Savin Goyal, Tech Lead for the ML Infra team at Netflix.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠

// Abstract:

In this conversation, Savin talked about some of the challenges encountered and choices made by the Netflix ML Infrastructure team while developing tooling for data scientists.

// Bio:

Savin is an engineer on the ML Infrastructure team at Netflix. He focuses on building generalizable infrastructure to accelerate the impact of data science at Netflix.

// Other links to check on Savin:
https://www.usenix.org/conference/opml20/presentation/cepoi
https://www.youtube.com/watch?v=lakPlz8GJcA&ab_channel=RConsortium
https://www.youtube.com/watch?v=-oMZAS9qfrE&ab_channel=AnalyticsIndiaMagazine
https://www.youtube.com/watch?v=yyWirT279tY&ab_channel=FunctionalTV
https://www.youtube.com/watch?v=QkRJ24Q0E-k&ab_channel=Matroid

----------- 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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Savin on LinkedIn: https://www.linkedin.com/in/savingoyal/

Timestamps:
[00:00] Background of Savin Goyal
[02:41] Breakdown of Metaflow
[05:44] In the stack, where does Metaflow stand?
[13:23] Where does Metaflow start in the Runway Project?
[15:27] What tools or storage does Netflix use for DataOps, ie, the front-end management of data sets, and how does that integrate with Metaflow? [18:56] Recommender Systems: Can you explain the other areas that you're using Machine Learning in?
[22:27] What do you feel is the hardest part of building an operating Machine Learning workflow? [28:45] 3 Pillars: Reproducibility, Scalability, Usability.
[36:05] You give so much power to people. How do you keep them from going overboard?
[37:47] Can you explain this Pillar of Usability?
[41:09] Road-based access control has been coming up a lot recently. Does Metaflow do something specific for that?
[44:49] What are some learnings that come across that you didn't have since you open-sourced when you were working at Netflix?
[48:10] What kind of trends have you been seeing? Where do you feel like the market is going?
[50:33] Have you seen some companies really interested in Metaflow? How have you been seeing them combine other tools that are out there?

  continue reading

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