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Why and When to Use Kubeflow for MLOps // Ryan Russon // Coffee Sessions #107

Jul 11, 2022

58:56

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Highlights

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Episode notes

Is Argo the Platform for Machine Learning?

01:39

Is There a Point of Maturity in the Cloud?

05:09

Is the Cupe Feuco System a Trend?

02:41

Introduction

5min

Cufo Is Not the Right Tool for the Job?

2min

Cube Flow

3min

CubeFlow

5min

The Onramp for Cube Flow

4min

Is the Cupe Feuco System a Trend?

3min

Pythons Out - Is That a Good Idea?

3min

Is Argo the Platform for Machine Learning?

2min

Do You Think Cuplos Is the Enstate?

3min

10.

Is There a Culo Existence?

3min

11.

Cube Flow

3min

12.

Cuflo Pipe Lines

3min

13.

Is There a Point of Maturity in the Cloud?

5min

14.

Air Flow Cuflo - Is That a Good Mix?

4min

15.

Is Cuflo the Biggest Emelops Challenge?

4min

16.

Best Practices for Machine Learning?

2min

17.

What Has Changed Over the Years?

4min

18.

Data Bricks - Is There a One Tool to Rule Them All?

3min

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