34 Episodio

  1. 📡 Building Scalable ML Models with Natanel Davidovits

    Pubblicato: 16/12/2024
  2. 💼 AI in the Enterprise with Jeremie Dreyfuss

    Pubblicato: 31/10/2024
  3. 🌲 Machine Learning in Agriculture: Scaling AI for Crop Management with Dror Haor

    Pubblicato: 15/09/2024
  4. 📊 Data-Driven Decisions: ML in E-Commerce Forecasting with Federico Bacci

    Pubblicato: 15/08/2024
  5. 🚗 Driving Innovation: Machine Learning in Auto Claims Processing

    Pubblicato: 15/07/2024
  6. 🚑 ML in the Emergency Room with Ljubomir Buturovic

    Pubblicato: 10/06/2024
  7. 🌊 AI-Native with Idan Gazit – The future of AI products and interfaces + Getting AI to production

    Pubblicato: 16/05/2024
  8. 🍪 Machine Learning in the cookie-less era with Uri Goren

    Pubblicato: 18/04/2024
  9. 🛰️ Modern & Realistic MLOps with Han-chung Lee

    Pubblicato: 18/03/2024
  10. 🩻 AI in Medical Devices & Medicine with Mila Orlovsky

    Pubblicato: 15/02/2024
  11. ⏪ Making LLMs Backwards Compatible with Jason Liu

    Pubblicato: 15/01/2024
  12. 🔴 Live MLOps Podcast – Building, Deploying and Monitoring Large Language Models with Jinen Setpal

    Pubblicato: 06/09/2023
  13. Live MLOps Podcast Episode!

    Pubblicato: 28/08/2023
  14. ⛹️‍♂️ Large Scale Video ML at WSC Sports with Yuval Gabay

    Pubblicato: 07/08/2023
  15. 🤖 GPTs & Large Language Models in production with Hamel Husain

    Pubblicato: 20/06/2023
  16. 🫣 Is Data Science a dying job? with Almog Baku

    Pubblicato: 23/05/2023
  17. 🏃‍♀️Moving Fast and Breaking Data with Shreya Shankar

    Pubblicato: 30/03/2023
  18. 🚴‍♀️ Quick & Dirty Machine Learning with Noa Weiss

    Pubblicato: 21/02/2023
  19. ✍️ Building ML Teams and Platforms with Assaf Pinhasi

    Pubblicato: 23/01/2023
  20. 🎨 Stable Diffusion and generative models with David Marx

    Pubblicato: 19/01/2023

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A podcast from DagsHub about bringing machine learning into the real world. Each episode features a conversation with top data science and machine learning practitioners, who'll share their thoughts, best practices, and tips for promoting machine learning to production

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