Practical AI: Machine Learning, Data Science, LLM

MLOps and tracking experiments with Allegro AI

Jul 20, 2020

51:08

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Episode notes

Introduction

5min

Allegro AI: CEO and Co-Founder

2min

The Importance of High Bars in the AI Space

3min

Allegro.i

3min

The Challenges of Convincing Data Scientists to Do Things Differently

3min

The Rise of the Data Science Team

3min

What Is MLOps?

5min

The Importance of Documentation in DevOps

2min

The Struggles of Naming Things

1min

10.

How to Lower the Barrier of Entry for AI Developers

3min

11.

The Importance of a Data Scientist in DevOps

2min

12.

The Holy Grail of Data Science

3min

13.

The Holy Grail of Data Versioning

2min

14.

Allegro's Open Source Platform for Data Science

2min

15.

Allegro Trains: How It Changes How You Implement It

3min

16.

Allegro Trains: How to Use It to Track Machines and Trains

2min

17.

How to Build an Enterprise Business on Top of Open Source Software

3min

18.

How to Be a Better Developer With PyTorch

2min

DevOps for deep learning is well… different. You need to track both data and code, and you need to run multiple different versions of your code for long periods of time on accelerated hardware. Allegro AI is helping data scientists manage these workflows with their open source MLOps solution called Trains. Nir Bar-Lev, Allegro’s CEO, joins us to discuss their approach to MLOps and how to make deep learning development more robust.

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Featuring:

Nir Bar-Lev – LinkedIn
Chris Benson – Twitter, GitHub, LinkedIn, Website
Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

Something missing or broken? PRs welcome!

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Practical AI: Machine Learning, Data Science, LLM

MLOps and tracking experiments with Allegro AI

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