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İçerik Demetrios Brinkmann tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Demetrios Brinkmann veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.
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"Real-Time" ML: Features and Inference // Sasha Ovsankin and Rupesh Gupta // MLOps Podcast #135

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Manage episode 349311171 series 3241972
İçerik Demetrios Brinkmann tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Demetrios Brinkmann veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

MLOps Coffee Sessions #135 with Sasha Ovsankin and Rupesh Gupta, Real-time Machine Learning: Features and Inference co-hosted by Skylar Payne.
// Abstract
Moving from batch/offline Machine Learning to more interactive "near" real-time requires knowledge, team, planning, and effort. We discuss what it means to do real-time inference and near-real-time features when to do this move, what tools to use, and what steps to take.
// Bio
Sasha Ovsankin Sasha is currently a Tech Lead of Machine Learning Model Serving infrastructure at LinkedIn, worked also on Feathr Feature Store, Real-Time Feature pipelines, designed metric platforms at LinkedIn and Uber, and was co-founder in two startups. Sasha is passionate about AI, Software Craftsmanship, improvisational music, and many more things.
Rupesh Gupta
Rupesh is a Sr. Staff Engineer in the AI team at LinkedIn. He has 10 years of experience in search and recommender systems.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs

// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
--------------- ✌️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, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
Connect with Sasha on LinkedIn: https://www.linkedin.com/in/sashao/
Connect with Rupesh on LinkedIn: https://www.linkedin.com/in/guptarupesh
Timestamps:
[00:00] Sasha's and Rupesh's preferred coffee
[01:30] Takeaways
[07:23] Changes in LinkedIn
[09:21] "Real-time" Machine Learning in LibnkedIn
[13:08] Value of Feedback
[14:24] Technical details behind getting the most recent information integrated into the models
[16:53] Embedding Vector Search action occurrence
[18:33] Meaning of "Real-time" Features and Inference
[20:23] Are "Real-time" Features always worth that effort and always helpful?
[23:22] Importance of model application
[25:26] Challenges in "Real-time" Features
[30:40] System design review on Pinterest
[36:13] Successes of real-time features
[38:31] Learnings to share
[45:52] Branching for Machine Learning
[48:44] Not so talked about discussion of "Real-time"
[51:09] Wrap up

  continue reading

321 bölüm

Artwork
iconPaylaş
 
Manage episode 349311171 series 3241972
İçerik Demetrios Brinkmann tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Demetrios Brinkmann veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

MLOps Coffee Sessions #135 with Sasha Ovsankin and Rupesh Gupta, Real-time Machine Learning: Features and Inference co-hosted by Skylar Payne.
// Abstract
Moving from batch/offline Machine Learning to more interactive "near" real-time requires knowledge, team, planning, and effort. We discuss what it means to do real-time inference and near-real-time features when to do this move, what tools to use, and what steps to take.
// Bio
Sasha Ovsankin Sasha is currently a Tech Lead of Machine Learning Model Serving infrastructure at LinkedIn, worked also on Feathr Feature Store, Real-Time Feature pipelines, designed metric platforms at LinkedIn and Uber, and was co-founder in two startups. Sasha is passionate about AI, Software Craftsmanship, improvisational music, and many more things.
Rupesh Gupta
Rupesh is a Sr. Staff Engineer in the AI team at LinkedIn. He has 10 years of experience in search and recommender systems.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs

// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
--------------- ✌️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, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
Connect with Sasha on LinkedIn: https://www.linkedin.com/in/sashao/
Connect with Rupesh on LinkedIn: https://www.linkedin.com/in/guptarupesh
Timestamps:
[00:00] Sasha's and Rupesh's preferred coffee
[01:30] Takeaways
[07:23] Changes in LinkedIn
[09:21] "Real-time" Machine Learning in LibnkedIn
[13:08] Value of Feedback
[14:24] Technical details behind getting the most recent information integrated into the models
[16:53] Embedding Vector Search action occurrence
[18:33] Meaning of "Real-time" Features and Inference
[20:23] Are "Real-time" Features always worth that effort and always helpful?
[23:22] Importance of model application
[25:26] Challenges in "Real-time" Features
[30:40] System design review on Pinterest
[36:13] Successes of real-time features
[38:31] Learnings to share
[45:52] Branching for Machine Learning
[48:44] Not so talked about discussion of "Real-time"
[51:09] Wrap up

  continue reading

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