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