Machine Learning Engineered

By Charlie You

This podcast helps Machine Learning Engineers become the best at what they do. Join host Charlie You every week as he talks to the brightest minds in data science, artificial intelligence, and software engineering to discover how they bring cutting edge research out of the lab and into products that people love. You'll learn the skills, tools, and best practices you can use to build better ML systems and accelerate your career in this flourishing new field.

  1. 1.
    A Practical Approach to Learning Machine Learning with Radek Osmulski (Earth Species Project)
    1:38:02
  2. 2.
    From Data Science Leader to ML Researcher with Rodrigo Rivera (Skoltech ADASE, Samsung NEXT)
    1:23:53
  3. 3.
    The Future of ML and AI Infrastructure and Ethics with Dan Jeffries (Pachyderm, AI Infrastructure Alliance)
    1:36:50
  4. 4.
    Developing Feast, the Leading Open Source Feature Store, with Willem Pienaar (Gojek, Tecton)
    1:11:49
  5. 5.
    Bringing DevOps Best Practices into Machine Learning with Benedikt Koller from ZenML
    1:28:18
  6. 6.
    Starting an Independent AI Research Lab with Josh Albrecht from Generally Intelligent
    1:24:53
  7. 7.
    Industrial Machine Learning and Building Tools for Data and Model Monitoring with Evidently AI Co-Founders Elena Samuylova and Emeli Dral
    1:21:15
  8. 8.
    Managing Data Science Teams and Hiring Machine Learning Engineers with Harikrishna Narayanan (YC Stealth Startup)
    1:15:35
  1. 9.
    Lessons Learned From Hosting the ML Engineered Podcast (Charlie Interviewed on the ML Ops Community podcast)
    1:03:58
  2. 10.
    Building a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio)
    1:15:25
  3. 11.
    Best of ML Engineered in 2020 Part 1 - ML Engineering
    1:13:09
  4. 12.
    Solocast - Holiday Gratitude
    12:36
  5. 13.
    Music Information Retrieval at Spotify and the Future of ML Tooling with Andreas Jansson of Replicate
    1:33:39
  6. 14.
    Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"
    1:22:53
  7. 15.
    Coding Career Tactics - Salary Negotiation, Public Speaking, and Creating Your Own Luck w/ Shawn "swyx" Wang (AWS)
    1:43:48
  8. 16.
    Yannic Kilcher: Explaining Papers on Youtube, Why Peer Review is Broken, and the Future of the Field
    1:32:21
  9. 17.
    How to Get Ahead in Machine Learning with Zak Slayback (1517 Fund)
    1:42:35
  10. 18.
    Why Multi-Modality is the Future of Machine Learning w/ Letitia Parcalabescu (University of Heidelberg, AI Coffee Break)
    1:31:47
  11. 19.
    Moin Nadeem (MIT): The extraordinary future of natural language models
    1:24:40
  12. 20.
    Peiyuan Liao: The 20 Year-Old Kaggle Grandmaster
    1:15:00
  13. 21.
    Shreya Shankar: Lessons learned after a year of putting ML into production
    1:24:00
  14. 22.
    Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production
    1:09:22
  15. 23.
    Sanyam Bhutani: Chai Time Data Science
    1:28:23
  16. 24.
    Devon Bernard: "If you can sell it, I can build it"
    1:42:02
  17. 25.
    Catherine Yeo: Fairness in AI and Algorithms
    1:03:27
  18. 26.
    Charles Yang: Machine Learning for Scientific Research
    1:26:10
  19. 27.
    swyx (Shawn Wang): Coding Career Strategy
    50:12
  20. 28.
    Solocast: Learning Machine Learning
    36:22
  21. 29.
    Karthik Suresh: Advice for Computer Science Students
    1:13:27
  22. 30.
    Jordan Dunne: What Engineers Should Know about Product and Program Management
    1:28:13
  23. 31.
    Introducing Machine Learning Engineered
    1:13

Listen to Machine Learning Engineered now.

Listen to Machine Learning Engineered in full in the Spotify app