Episode

Johannes Otterbach — Unlocking ML for Traditional Companies

Podcast
Gradient Dissent: Conversations on AI
Published
May 12, 2022
Duration seconds
2690
Processing state
failed
Canonical source
https://wandb.ai/site/resources/podcast
Audio
https://podcasts.captivate.fm/media/e5aa451c-49fe-4092-8610-c3ed37bee6ce/GD-Johannes-V1.mp3
JSON
/v1/public/podcasts/gradient-dissent/episodes/johannes-otterbach-unlocking-ml-for-traditional-companies
Markdown
/podcast/gradient-dissent/johannes-otterbach-unlocking-ml-for-traditional-companies.md

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Summary

Johannes Otterbach is VP of Machine Learning Research at Merantix Momentum, an ML consulting studio that helps their clients build AI solutions. Johannes and Lukas talk about Johannes' background in physics and applications of ML to quantum computing, why Merantix is investing in creating a cloud-agnostic tech stack, and the unique challenges of developing and deploying models for different customers. They also discuss some of Johannes' articles on the impact of NLP models and the future of AI regulations. Show notes (transcript and links): http://wandb.me/gd-johannes-otterbach --- ⏳ Timestamps: 0:00 Intro 1:04 Quantum computing and ML applications 9:21 Merantix, Ventures, and ML consulting 19:09 Building a cloud-agnostic tech stack 24:40 The open source tooling ecosystem 30:28 Handing off models to customers 31:42 The impact of NLP models on the real world 35:40 Thoughts on AI and regulation 40:10 Statistical physics and optimization problems 42:50 The challenges of getting high-quality data 44:30 Outro --- Connect with Johannes: 📍 LinkedIn: https://twitter.com/jsotterbach 📍 Personal website: http://jotterbach.github.io/ 📍 Careers at Merantix Momentum: https://merantix-momentum.com/about#jobs --- 💬 Host: Lukas Biewald 📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla --- Subscribe and listen to our podcast today! 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Google Podcasts: http://wandb.me/google-podcasts​ 👉 Spotify: http://wandb.me/spotify​