Bringing Audio ML Models into Production

Name: Bringing%20Audio%20ML%20Models%20into%20Production
Uploaded: 2022-04-01

Posted Apr 01, 2022 | Views 884

# Auto MLOps

# Automate Data

# ML Orchestration

# Utopia Music

# thesoundofai.com

speakers

Valerio Velardo

AI Music Consultant + Host of The Sound of AI + MLOps Lead @ The Sound of AI, Utopia Music (MLOps)

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, he 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).

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Demetrios Brinkmann

Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

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.

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