// 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 https://medium.com/@byron.allen

// MLOps Jobs board  

https://mlops.pallet.xyz/jobs

MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

--------------- ✌️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

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with George on LinkedIn: https://www.linkedin.com/in/george-pearse-b7a76a157/?originalSubdomain=uk

Connect with Byron on LinkedIn: https://www.linkedin.com/in/byronaallen/

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