96 Epizód

  1. Data Meshes, Fabrics, and Discovery with Zhamak Dehghani, David Thomas, and Shirshanka Das

    Közzétéve: 2022. 05. 04.
  2. Investing in Communities, Differentiating, and Trusting Your Gut with Erica Brescia

    Közzétéve: 2022. 04. 27.
  3. Data on Kubernetes with Kelsey Hightower, Lachlan Evenson, and Patrick McFadin

    Közzétéve: 2022. 04. 20.
  4. Deep Fakes, Responsible Data Science, and Trust with David Danks

    Közzétéve: 2022. 04. 13.
  5. Cloud Innovation, Analytics, and Data Transformation with Monica Kumar

    Közzétéve: 2022. 03. 30.
  6. Data Lakehouses, Interoperability, and Accessibility with Tomer Shiran

    Közzétéve: 2022. 03. 16.
  7. Interoperability, Governance, and Divergent Teams with Prukalpa Sankar

    Közzétéve: 2022. 03. 02.
  8. Trust, Automation, and Trade-Offs with Joseph Jacks

    Közzétéve: 2022. 02. 16.
  9. Open Source, Adoptability, and Name Changes with Martin Traverso

    Közzétéve: 2022. 02. 02.
  10. Season Two Finale and Recap with Open||Source||Data Producer Audra Montenegro

    Közzétéve: 2021. 10. 29.
  11. Embeddings, Feature stores, and MLOps with Simba Khadder

    Közzétéve: 2021. 10. 14.
  12. Abundance, Metadata, and Automation with Mark Grover

    Közzétéve: 2021. 09. 30.
  13. Metadata, Communities, and Architecture with Shirshanka Das

    Közzétéve: 2021. 09. 16.
  14. Data Management Pain Points and Future Solutions for Data Discovery

    Közzétéve: 2021. 09. 02.
  15. ModelOps, ML Monitoring, and Busy Humans with Elena Samuylova

    Közzétéve: 2021. 08. 19.
  16. Cloud-Native, Open-Source, and Collaborative with Eric Brewer and Melody Meckfessel

    Közzétéve: 2021. 08. 05.
  17. MLOps, AIOps, and Data Startups with Jocelyn Goldfein

    Közzétéve: 2021. 07. 22.
  18. Git-Like Branch and Merge for Data with Einat Orr

    Közzétéve: 2021. 07. 08.
  19. Data Discoverability, Products, and User Diversity with Shinji Kim

    Közzétéve: 2021. 06. 24.
  20. Data Observability, Customer-Led Growth, and Confidence with Barr Moses

    Közzétéve: 2021. 06. 10.

4 / 5

What can we learn from ai-native development through stimulating conversations with developers, regulators, academics and people like you that drive forward development, seek to understand impact, and are working to mitigate risk in this new world? Join Charna Parkey and the community shaping the future of open source data, open source software, data in AI, and much more.

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