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This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 3.
// Diagram Link:
https://github.com/dmangonakis/mlops-lab-example-yelp
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Connect with Laszlo on LinkedIn https://www.linkedin.com/in/laszlosragner/
Connect with Artem on LinkedIn: https://www.linkedin.com/in/artem-yushkovsky/
Connect with Paulo on LinkedIn: https://www.linkedin.com/in/paulo-maia-410874119/
Connect with Dimi on LinkedIn:
Timestamps:
[00:00] Engineering Labs Recap Team Three
[01:12] Laszlo Sranger Background
[02:05] Artem Background
[04:45] Dimi Background
[06:31] Paulo Background
[08:51] Initial Product Ideas Overview
[09:12] Decent Product Using Yelp Dataset
[10:32] Backend Facade Streamlit Overview
[13:52] Questioning Bad Practices
[14:11] Demo Works But Limited
[15:12] Walking Through Streamlit Code
[15:16] Decoupled Frontend Backend Architecture
[16:54] Managerial Considerations
[19:00] Working Outside Comfort Zones
[20:36] Key Takeaways From Lab
[20:42] MLflow Architecture Insights
[22:21] Additional Considerations
[22:31] MLflow End-to-End Monitoring
[24:50] Explainability Tools and Complexity
[26:29] Real-World Issues
[26:36] Avoid Unnecessary Bells and Whistles
[28:33] Difficulties in Process
[30:25] Engineering Mistakes Reflection
[31:17] Artifact Logging Challenges
[32:00] Identifying Non-Ideal Aspects
[33:21] PyTorch Limitations
[34:52] Managing Dependencies
[35:08] Avoid Using Notebooks
[36:27] Consistent Scripts And Environments
[37:08] Replicable Docker Processes
[37:42] Future MLflow Use
[38:23] MLflow Improvement Over Time
[40:34] Kubernetes Knowledge Requirements
[41:25] Kubernetes Provides Great Output
[46:03] Current Status Limitations
[46:53] Limited Production Control
[47:40] Kubernetes Knowledge For Data Scientists
[48:14] Machine Learning Cultural Movement
[50:55] Jack Of All Trades
[51:32] Productized ML Requires Engineering
[56:27] Final Lab Reflections
[57:11] Cloud Credits For Next Lab
