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Bringing Audio ML Models into Production // Valerio Velardo // MLOps Coffee Sessions #90

Apr 4, 2022

50:45

Creator website

Highlights

AI Chapters

Episode notes

I'm Preaching to the Choir, Right?

03:58

The Machine Learning Life Cycle Within Music Tec Companies?

02:42

Music Engineers and Data Scientists - What Have Been Some Challenges?

02:55

Introduction

3min

Music

2min

How Did Computer Science and Artificial Intelligence Get Into Machine Learning?

4min

Machine Learning

2min

Using Artificial Intelligence, Oroe, or Digital Signa Processing, to Extract Information From Music

5min

The Machine Learning Life Cycle Within Music Tec Companies?

3min

Transform Spectra Into ML Spectrograms

4min

How Do You See Machine Learning Being Better Serviced?

6min

Machine Learning for Scaleeps and Enterprises

3min

10.

How Many Use Cases Can You Build From Configuration Rite?

5min

11.

Music Engineers and Data Scientists - What Have Been Some Challenges?

3min

12.

I'm Preaching to the Choir, Right?

4min

13.

Machine Learning Platforms - How Do You Solve Them?

4min

14.

Is That What You're Declaring When You Start a Project?

5min

MLOps Coffee Sessions #90 with Valerio Velardo, Bringing Audio ML Models into Production.

// Abstract
The majority of audio/music tech companies that employ ML still don’t use MLOps regularly. In these companies, you rarely find audio ML pipelines which take care of the whole ML lifecycle in a reliable and scalable manner. Audio ML probably pays the price of being a small sub-discipline of ML. It’s dwarfed by ML applications in image processing and NLP.

In audio ML, novelties tend to travel slowly. However, things are starting to change. A few audio and music tech companies are investing in MLOps. Building MLOps solutions for music presents unique challenges because audio data is significantly different from all other data types.

// Bio
Valerio is MLOps Lead at Utopia Music. He’s also an AI audio consultant who helps companies implement their AI music vision by providing technical, strategy, and talent sourcing services.

Valerio is interested both in the R&D and productization (MLOps) aspects of AI applied to the audio and music domains. He's the host of The Sound of AI, the largest YouTube channel and online community on AI audio with more than 22K subscribers.

Previously, Valerio founded and led Melodrive, a tech startup that developed an AI-powered music engine capable of generating emotion-driven video game music in real-time. Valerio earned a Ph.D. in music AI from the University of Huddersfield (UK).

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

// Related Links
Valerio's website
https://valeriovelardo.com/
The Sound of AI YouTube channel:
https://www.youtube.com/channel/UCZPFjMe1uRSirmSpznqvJfQ

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Valerio on LinkedIn: https://www.linkedin.com/in/valeriovelardo/

Timestamps:
[00:00] Introduction to Valerio Velardo
[01:28] Please subscribe and rate us!
[02:40] History of Valerio's love for music
[04:12] Intervention of computer science, AI, and Machine Learning in music
[08:06] Experimenting with Machine Learning
[09:25] Environmental Sound AI
[11:05] AI Music
[15:22] Traditional ML life cycle within music tech companies
[18:02] Representation of data
[22:22] Audio being better served in the market
[30:42] Success metrics
[35:17] Challenges when talking to R&D teams
[38:10] Things need to be battle-hardened before production
[39:09] Education process besides Valerio's Youtube channel
[42:38] Rectifying use cases not related to audio
[45:48] Organizing modular blocks building stacks
[47:59] Open-source tools implementation
[50:28] Wrap up