MLOps Engineering Labs Recap // Part 2 // MLOps Coffee Sessions #31

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletterThis 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  --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/register  Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/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