34 Epizód

  1. 📡 Building Scalable ML Models with Natanel Davidovits

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

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

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

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

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

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

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

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

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

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

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

    Közzétéve: 2023. 09. 06.
  13. Live MLOps Podcast Episode!

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

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

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

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

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

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

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

    Közzétéve: 2023. 01. 19.

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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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