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📌 MLOps live: biweekly Q&A for people doing MLOps at a reasonable scale*

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📌 MLOps live: biweekly Q&A for people doing MLOps at a reasonable scale*

get those juicy bits, dirty hacks, and pragmatic workarounds from ML people in the trenches

Apr 6, 2022
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📌 MLOps live: biweekly Q&A for people doing MLOps at a reasonable scale*

thesequence.substack.com
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*TheSequence is happy to support the new series of MLOps events by neptune.ai.


Okay, so you probably know by now that here at neptune.ai, we believe in a pragmatic, reasonable scale approach to MLOps. 

And there are not enough people who do ML and MLOps at a reasonable scale who share their experiences online.

You can read great stuff from super-advanced companies, but the truth is most ML folks are not solving hyperscale production problems. 

They are either doing production ML at no scale, or at a reasonable scale. You are probably doing it at a reasonable scale yourself. 

We wrote many articles about MLOps on our blog, but we wanted to go deeper.

You see, articles and presentations are great, but the most interesting part of any talk is the Q&A (and there is not enough time for that like ever). 

We wanted to get those juicy bits, dirty hacks, and pragmatic workarounds from ML people in the trenches. 

So we've decided to start a live, biweekly Q&A event where we get practitioners to answer questions from other practitioners about one subject related to production ML.

This time we’ll talk with the father of reasonable scale MLOps Jacopo Tagliabue about… you guessed it, everything related to setting up MLOps at a reasonable scale :) 

Join us!

  • What: Live MLOps Q&A about: “Setting up MLOps at a reasonable scale”

  • Who: Jacopo Tagliabue, Director of AI at Coveo

  • When: Wednesday, April 13th | 6 PM CET

REGISTER NOW

See you there!

P.S.

If you cannot make it this week, don’t worry. We’ll have those sessions available as Youtube videos and Podcast episodes. Follow us on Twitter or Linkedin and stay in the loop. 

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📌 MLOps live: biweekly Q&A for people doing MLOps at a reasonable scale*

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