ML Flow vs Kubeflow 2022 // Byron Allen // Coffee Sessions

MLOps Coffee Sessions #108 with Byron Allen, AI & ML Practice Lead at Contino, ML Flow vs Kubeflow 2022 co-hosted by George Pearse.
// Abstract
The amazing Byron Allen talks to us about why MLflow and Kubeflow are not playing the same game!
ML flow vs Kubeflow is more like comparing apples to oranges or as he likes to make the analogy they are both cheese but one is an all-rounder and the other a high-class delicacy. This can be quite deceiving when analyzing the two. We do a deep dive into the functionalities of both and the pros/cons they have to offer.
// Bio
Byron wears several hats. AI & ML practice lead, solutions architect, ML engineer, data engineer, data scientist, Google Cloud Authorized Trainer, and scrum master. He has a track record of successfully advising on and delivering data science platforms and projects. Byron has a mix of technical capability, business acumen, and communication skills that make me an effective leader, team player, and technology advocate.
See Byron write at / byron.allen
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Timestamps:
[00:00] Introduction to Byron Allen
[01:10] Introduction to the new co-host George Pearse
[01:41] ML Flow vs Kubeflow
[05:40] George's take on ML Flow and Kubeflow
[07:28] Writing in YAML
[09:47] Developer experience
[13:38] Changes in ML Flow and Kubeflow
[17:58] Messing around ML Flow Serving
[20:00] A taste of Kubeflow through K-Serve
[23:18] Managed service of Kubeflow
[25:15] How George used Kubeflow
[27:45] Getting the Managed Service
[31:30] Getting Authentication
[32:41] ML Flow docs vs Kubeflow docs
[36:59] Kubeflow community incentives
[42:25] MLOps Search term
[42:52] Organizational problem
[43:50] Final thoughts on ML Flow and Kubeflow
[49:19] Bonus
[49:35] Entity-Centric Modeling
[52:11] Semantic Layer options
[57:27] Semantic Layer with Machine Learning
[58:40] Satellite Infra Images demo
[1:00:49] Motivation to move away from SQL
[1:03:00] Managing SQL
[1:05:24] Wrap up

[@mage1over137]

It's in the name, Kubeflow is harder because it uses kubernetes which is much more complex than managing a conda environment. They also solve different problems and are used by different groups of people. ML flow really supports the development of and packaging of models, while Kubeflow is used orchestrate the infrastructure to support training and deploying models, which is a lot harder to do. You need dev ops because ultimately you weren't given sufficient permissions.

[@dec13666]

Nice insight. Without knowing, you've just pretty much summarized this video 😅👍
Thanks! 🤝

[@revanthtiruveedhi3842]

Great video! Thanks

[@jetb179]

It was cioa cheese