MLOps Coffee Sessions #107 with Ryan Russon, Manager, MLOps and Data Science of Maven Wave Partners, Why and When to Use Kubeflow for MLOps co-hosted by Mihail Eric.  

// Abstract
Kubeflow is an excellent platform if your team is already leveraging Kubernetes and allows for a truly collaborative experience.

Let’s take a deep dive into the pros and cons of using Kubeflow in your MLOps.  

// Bio
From serving as an officer in the US Navy to Consulting for some of America's largest corporations, Ryan has found his passion in the enablement of Data Science workloads for companies and teams.     

Having spent years as a data scientist, Ryan understands the types of challenges that DS teams face in scaling, tracking, and efficiently running their workloads.  

// MLOps Jobs board  
https://mlops.pallet.xyz/jobs

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
https://www.mavenwave.com/
https://go.mlops.community/hFApDb

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Ryan on LinkedIn: https://www.linkedin.com/in/ryanrusson/

Timestamps:
[00:00] Introduction to Ryan Russon
[01:13] Takeaways
[04:17] Bullish on KubeFlow!
[06:23] KubeFlow in ML tooling
[11:47] Kubeflow having its velocity
[14:16] To Kubeflow or not to Kubeflow
[18:25] KubeFlow ecosystem maturity
[20:51] Alternatively starting from scratch?
[23:11] Argo workflow vs KubeFlow pipelines
[25:08] KubeFlow as an end-state for citizen data scientists
[28:24] End-to-end workflow key players  
[31:17] K-serve
[33:41] KubeFlow on orchestrators
[36:24] Natural transition to KubeFlow maturity
[41:33] "Don't forget about the engineer cost."
[42:21] KubeFlow to other "Flow brothers" trade-offs
[46:12] Biggest MLOps challenge
[49:52] Best practices around file structure
[52:15] KubeFlow changes over the years and what to expect moving forward
[55:52] Best-of-breed vision
[57:54] Wrap up