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MLOps Engineering Labs Recap // Part 1 // MLOps Coffee Sessions #30

MLOps Engineering Labs Recap // Part 1 // MLOps Coffee Sessions #30

FromMLOps.community


MLOps Engineering Labs Recap // Part 1 // MLOps Coffee Sessions #30

FromMLOps.community

ratings:
Length:
60 minutes
Released:
Feb 23, 2021
Format:
Podcast episode

Description

This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 1.

// Diagram Link: https://github.com/mlops-labs-team1/engineering.labs#workflow

--------------- ✌️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 Alexey on LinkedIn: https://www.linkedin.com/in/alexeynaiden/
Connect with John on LinkedIn: https://www.linkedin.com/in/johnsavageireland/
Connect with Michel on LinkedIn: https://www.linkedin.com/in/michel-vasconcelos-8273008/
Connect with Varuna on LinkedIn: https://www.linkedin.com/in/vpjayasiri/

Timestamps
[00:00] Introduction to Engineering Labs Participants
[00:34] What are the Engineering Labs?
[01:05] Credits to Ivan Nardini who organized this episode!  
[04:24] John Savage Profile
[05:13] Did you want to learn MLFlow before this?
[05:50] Alexey Naiden Profile  
[07:26] Varuna Jayasiri Profile
[08:28] Michel Vasconcelos Profile
[10:07] Do something with Pytorch and MLFlow and then figure out the rest: What did the process look like for you all?  What have you created?
[13:39] What did the implementation look like? How you went about structuring and coding it?
[17:03] Did you encounter problems along the way?
[20:26] Can you give us a rough overview of what you designed and then where was the first problem you saw?
[23:08] Was there a lot to catch up with or did you feel it was fine. Can you explain how it was?
[24:12] Talk to us about this tool that you have that John was calling out. What was it called?
[24:41] Is this homegrown? You built this?
[24:51] Did you guys implement this when you went to the engineering labs? [26:03] Can you take us through the pipeline and then the serving and what the overall view of the diagram is?
[37:26] For a pet project it works well, but when you wanna start adding a little bit more on top of it wasn't doing the trick?
[38:13] So you see it coming in it's much less of an integral part, another lego building block that is part of the whole thing?
[40:54] Did you all have trouble with Pytorch or MLFlow?
[42:44] Along with that, what was the prompt you were encountering when you were trying to use Torchserve?
[44:27] What are you thinking would have been better in that case?
[49:05] Feedback on how Engineering Labs went
[50:20] Michel: "Engineering Labs should go on. I would like to be a part of it in the next lab."
[51:52] Varuna: "This gives me a tangible thing to look at at any point in time and learn from it."
[53:00] John: "I feel I have an anchor into the world of MLOps from having done this lab."
[55:52] Alexey: "We're at a checkpoint where there are ways we could take"
[56:01] Terraform piece Michel wrote for reproducibility.
Released:
Feb 23, 2021
Format:
Podcast episode

Titles in the series (100)

Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.