The MLOps Podcast

A podcast by Dean Pleban @ DagsHub

Categories:

32 Episodes

  1. 🌲 Machine Learning in Agriculture: Scaling AI for Crop Management with Dror Haor

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Published: 1/19/2023
  19. πŸ”΄πŸŸ’πŸŸ£Julia Language in Production with Logan Kilpatrick

    Published: 11/21/2022
  20. πŸ›  Building tools for MLOps with Guy Smoilovsky

    Published: 10/18/2022

1 / 2

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