34 Episodes

  1. πŸ“‘ Building Scalable ML Models with Natanel Davidovits

    Published: 12/16/2024
  2. πŸ’Ό AI in the Enterprise with Jeremie Dreyfuss

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

    Published: 9/15/2024
  4. πŸ“Š Data-Driven Decisions: ML in E-Commerce Forecasting with Federico Bacci

    Published: 8/15/2024
  5. πŸš— Driving Innovation: Machine Learning in Auto Claims Processing

    Published: 7/15/2024
  6. πŸš‘ ML in the Emergency Room with Ljubomir Buturovic

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

    Published: 5/16/2024
  8. πŸͺ Machine Learning in the cookie-less era with Uri Goren

    Published: 4/18/2024
  9. πŸ›°οΈ Modern & Realistic MLOps with Han-chung Lee

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

    Published: 2/15/2024
  11. βͺ Making LLMs Backwards Compatible with Jason Liu

    Published: 1/15/2024
  12. πŸ”΄ Live MLOps Podcast – Building, Deploying and Monitoring Large Language Models with Jinen Setpal

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

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

    Published: 8/7/2023
  15. πŸ€– GPTs & Large Language Models in production with Hamel Husain

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

    Published: 5/23/2023
  17. πŸƒβ€β™€οΈMoving Fast and Breaking Data with Shreya Shankar

    Published: 3/30/2023
  18. πŸš΄β€β™€οΈ Quick & Dirty Machine Learning with Noa Weiss

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

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

    Published: 1/19/2023

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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