• Unifying structured and unstructured data for AI: Rethinking ML infrastructure with Nikhil Simha and Varant Zanoyan

  • 2024/08/30
  • 再生時間: 1 時間 2 分
  • ポッドキャスト

Unifying structured and unstructured data for AI: Rethinking ML infrastructure with Nikhil Simha and Varant Zanoyan

  • サマリー


  • Summary

    In this episode, we dive deep into the future of data infrastructure for AI and ML with Nikhil Simha and Varant Zanoyan, two seasoned engineers from Airbnb and Facebook. Nikhil and Varant share their journey from building real-time data systems and ML infrastructure at tech giants to launching their own venture.

    The conversation explores the intricacies of designing developer-friendly APIs, the complexities of handling both batch and streaming data, and the delicate balance between customer needs and product vision in a startup environment.

    Contacts & Links

    Nikhil Simha
    Varant Zanoyan
    Chronon project

    Chapters

    00:00 Introduction and Past Experiences
    04:38 The Challenges of Building Data Infrastructure for Machine Learning
    08:01 Merging Real-Time Data Processing with Machine Learning
    14:08 Backfilling New Features in Data Infrastructure
    20:57 Defining Failure in Data Infrastructure
    26:45 The Choice Between SQL and Data Frame APIs
    34:31 The Vision for Future Improvements
    38:17 Introduction to Chrono and Open Source
    43:29 The Future of Chrono: New Computation Paradigms
    48:38 Balancing Customer Needs and Vision
    57:21 Engaging with Customers and the Open Source Community
    01:01:26 Potential Use Cases and Future Directions

    Click here to view the episode transcript.

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あらすじ・解説


Summary

In this episode, we dive deep into the future of data infrastructure for AI and ML with Nikhil Simha and Varant Zanoyan, two seasoned engineers from Airbnb and Facebook. Nikhil and Varant share their journey from building real-time data systems and ML infrastructure at tech giants to launching their own venture.

The conversation explores the intricacies of designing developer-friendly APIs, the complexities of handling both batch and streaming data, and the delicate balance between customer needs and product vision in a startup environment.

Contacts & Links

Nikhil Simha
Varant Zanoyan
Chronon project

Chapters

00:00 Introduction and Past Experiences
04:38 The Challenges of Building Data Infrastructure for Machine Learning
08:01 Merging Real-Time Data Processing with Machine Learning
14:08 Backfilling New Features in Data Infrastructure
20:57 Defining Failure in Data Infrastructure
26:45 The Choice Between SQL and Data Frame APIs
34:31 The Vision for Future Improvements
38:17 Introduction to Chrono and Open Source
43:29 The Future of Chrono: New Computation Paradigms
48:38 Balancing Customer Needs and Vision
57:21 Engaging with Customers and the Open Source Community
01:01:26 Potential Use Cases and Future Directions

Click here to view the episode transcript.

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