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Data Engineering for ML // Chad Sanderson // Coffee Sessions #117

Aug 19, 2022

58:16

Creator website

Highlights

AI Chapters

Episode notes

Data Engineering - I Love It

02:16

Semantic Events - The Product Use Case

02:44

AWS and the Data Team Live in Totally Different Worlds, Right?

05:31

Introduction

2min

The Importance of Data Modeling in MLOps

2min

A Convy Approach to Data Modeling

5min

The Data Warehouse Modeling Toolkit

2min

The Core of the Problem, Right?

5min

Is There a Need to Throw the Baby Out With the Bathwater?

4min

Is There a Disconnect Chat?

3min

Data Platform Lead - The Oliver Twist Thing

2min

The Data Engineers Aren't Listening to Me.

4min

10.

Data Engineering for ML Course - What's Up, Bud?

4min

11.

The Core of Your Question Is Lineage

4min

12.

Data Quality and Data Validation - The Biggest Problem in Data Science

3min

13.

ML Engineering - You Can't Do This to Me. Come On.

5min

14.

Data Engineering - I Love It

2min

15.

AWS and the Data Team Live in Totally Different Worlds, Right?

6min

16.

Semantic Events - The Product Use Case

3min

17.

I'm Sorry to Be a Breaking Record, Right?

2min

MLOps Coffee Sessions #117 with Chad Sanderson, Head of Product, Data Platform at Convoy, Data Engineering for ML co-hosted by Josh Wills.

// Abstract
Data modeling is building relationships between core concepts within your data. The physical data model shows how the relationships manifest in your data environment but then there's the semantic data model, the way that entity relationship design is extracted away from any data-centric implementation.

Let's do the good old fun of talking about why data modeling is so important!

// Bio
Chad Sanderson is the Product Lead for Convoy's Data Platform team, which includes the data warehouse, streaming, BI & visualization, experimentation, machine learning, and data discovery.

Chad has built everything from feature stores, experimentation platforms, metrics layers, streaming platforms, analytics tools, data discovery systems, and workflow development platforms. He’s implemented open source, SaaS products (early and late-stage) and has built cutting-edge technology from the ground up. Chad loves the data space, and if you're interested in chatting about it with him, don't hesitate to reach out.

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

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

// Related Links
https://odsc.com/speakers/scaling-machine-learning-with-data-mesh/ https://docs.google.com/presentation/d/1rVtltHkRkP_JaGZdkAS3U_SXfr5Gg-RP980FKXh0YNU/edit?usp=sharing
Josh Wills will be teaching a course on Data Engineering for Machine Learning in September here:
https://www.getsphere.com/ml-engineering/data-engineering-for-machine-learning

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Josh on LinkedIn: https://www.linkedin.com/in/josh-wills-13882b/
Connect with Chad on LinkedIn: https://www.linkedin.com/in/chad-sanderson/

Timestamps:
[00:00] Introduction of the new co-host Josh Wills
[00:54] Introduction to Chad Sanderson
[01:46] Josh will lead a course for Machine Learning in mid-September
[02:16] Data modeling blog post of Chad
[06:10] Idea of Strategy
[09:40] Modern cloud data warehouses
[17:01] Layering on contracts
[20:38] Scaling at larger companies
[25:30] Carrot-stick strategy
[34:27] Second and third-order effects
[39:53] Stockholm Syndrome
[41:22] Quality checks at Slack
[45:28] Success in two main ways according to Chad
[47:35] Completely and utterly different universes
[53:42] Product use case to push semantic events
[56:00] Pattern of analysis of the sequence of events
[57:23] Wrap up