MLOps.community MLOps Engineering Labs Recap // Part 1 // MLOps Coffee Sessions #30
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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
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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 Engineering Labs
[01:05] Credits to Ivan Nardini
[04:24] John Savage Profile
[05:13] Prior MLflow Knowledge
[05:50] Alexey Naiden Profile
[07:26] Varuna Jayasiri Profile
[08:28] Michel Vasconcelos Profile
[10:07] Process Using PyTorch MLflow
[13:39] Implementation Structure and Coding
[17:03] Encountering Problems Along the Way
[20:26] Overview and First Problem
[23:08] Catching Up or Comfortable
[24:12] Tool John Called Out
[24:41] Homegrown Tool Confirmation
[24:51] Engineering Labs Implementation
[26:03] Pipeline and Serving Overview
[37:26] Pet Project Limitations
[38:13] Lego-Like Modular Building Block
[40:54] PyTorch or MLflow Troubles
[42:44] Torchserve Prompt Challenges
[44:27] Considering Better Approaches
[49:05] Feedback on Labs Experience
[50:20] Michel Wants Future Participation
[51:52] Varuna Values Tangible Learning
[53:00] John Anchored in MLOps
[55:52] Alexey Reaching Checkpoint
[56:01] Michel’s Terraform Reproducibility Piece