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Fixing Your ML Data Blind Spots // Yash Sheth // MLOps Coffee Sessions #102

Jun 10, 2022

52:16

Creator website

Highlights

AI Chapters

Episode notes

Deep Learning and the Post Training Phase

05:09

How to Cure Unstructured Data?

02:19

Machine Learning and Deep Learning - What's Your Take on Synthetic Data?

04:26

Introduction

3min

Machine Learning and Deep Learning - What's Your Take on Synthetic Data?

4min

The Challenges in the Instructor Data

4min

How to Cure Unstructured Data?

2min

Deep Learning and the Post Training Phase

5min

The Difference Between Small Teams and Large Teams

3min

Data Specific Tooling That Can Help Data Scientists Quickly Understand Issues

3min

Unstructured Data - Is There a Specific Unstructure Data Problem?

2min

You Just Don't Need to Know Calculus to Be a Day a Scientist?

4min

10.

Synthetic Data and the Challenges It Faces in Unstructured Data

5min

11.

What Kinds of Users Benefit the Most From Galileo?

3min

12.

I Need One Slide, Give Me the Benefits and Why We Need It

3min

13.

Machine Learning - Data Centric Focus Is a Key Way to Succeed

4min

14.

The Trade of Uh, by Kevin Many I Have Here

3min

15.

The Most Ridiculous Thing You Almost Did on the Marketing Side?

2min

16.

I'm Quite Foolish on Quantum Computing, Right?

2min

MLOps Coffee Sessions #102 with Yash Sheth, Fixing Your ML Data Blindspots co-hosted by Adam Sroka.

// Abstract
Improving your dataset quality is absolutely critical for effective ML. Finding errors in your datasets is generally a slow, iterative, and painstaking process.

Data scientists should be proactively fixing their model’s blindspots by improving their training data. In this talk, Yash discusses how Galileo helps data scientists identify, fix, and track data across the entire ML workflow.

// Bio
Co-founder and VP of Engineering. Prior to starting Galileo, Yash spent the last decade working on Automatic Speech Recognition (ASR) at Google, leading their core speech recognition platform team, that powers speech-to-text across 20+ products at Google in over 80 languages along with thousands of businesses through their Cloud Speech API.

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

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

// Related Links
Website: https://www.rungalileo.io/
Trade-Off: Why Some Things Catch On, and Others book by Kevin Maney:
https://www.amazon.com/Trade-Off-Some-Things-Catch-Others/dp/0385525958

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka/
Connect with Yash on LinkedIn: https://www.linkedin.com/in/yash-sheth-72111216/

Timestamps:
[00:00] Introduction to Yash Sheth
[02:53] Takeaways
[04:35] Why unstructured data?
[06:59] Fitting in the workflow
[10:56] Digging into the different pains
[18:23] Vision around the democratization of machine learning
[24:31] Unstructured data problem
[25:49] Galileo handling unified tools
[27:21] Calculus for ML
[28:45] Gatekeep
[29:49] Synthetic data in the unstructured data world of Galileo
[33:10] Tips for data scientists that have unstructured data but with a small data set
[35:00] Benefits of users from Galileo
[37:15] Business case for dummies
[42:36] War stories
[44:49] Rapid fire questions
[50:55] Wrap up