34 Episodo

  1. 📡 Building Scalable ML Models with Natanel Davidovits

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

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

    Publicado: 15/9/2024
  4. 📊 Data-Driven Decisions: ML in E-Commerce Forecasting with Federico Bacci

    Publicado: 15/8/2024
  5. 🚗 Driving Innovation: Machine Learning in Auto Claims Processing

    Publicado: 15/7/2024
  6. 🚑 ML in the Emergency Room with Ljubomir Buturovic

    Publicado: 10/6/2024
  7. 🌊 AI-Native with Idan Gazit – The future of AI products and interfaces + Getting AI to production

    Publicado: 16/5/2024
  8. 🍪 Machine Learning in the cookie-less era with Uri Goren

    Publicado: 18/4/2024
  9. 🛰️ Modern & Realistic MLOps with Han-chung Lee

    Publicado: 18/3/2024
  10. 🩻 AI in Medical Devices & Medicine with Mila Orlovsky

    Publicado: 15/2/2024
  11. ⏪ Making LLMs Backwards Compatible with Jason Liu

    Publicado: 15/1/2024
  12. 🔴 Live MLOps Podcast – Building, Deploying and Monitoring Large Language Models with Jinen Setpal

    Publicado: 6/9/2023
  13. Live MLOps Podcast Episode!

    Publicado: 28/8/2023
  14. ⛹️‍♂️ Large Scale Video ML at WSC Sports with Yuval Gabay

    Publicado: 7/8/2023
  15. 🤖 GPTs & Large Language Models in production with Hamel Husain

    Publicado: 20/6/2023
  16. 🫣 Is Data Science a dying job? with Almog Baku

    Publicado: 23/5/2023
  17. 🏃‍♀️Moving Fast and Breaking Data with Shreya Shankar

    Publicado: 30/3/2023
  18. 🚴‍♀️ Quick & Dirty Machine Learning with Noa Weiss

    Publicado: 21/2/2023
  19. ✍️ Building ML Teams and Platforms with Assaf Pinhasi

    Publicado: 23/1/2023
  20. 🎨 Stable Diffusion and generative models with David Marx

    Publicado: 19/1/2023

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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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