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Unifying structured and unstructured data for AI: Rethinking ML infrastructure with Nikhil Simha and Varant Zanoyan
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A tartalmat a Kostas Pardalis, Nitay Joffe biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Kostas Pardalis, Nitay Joffe vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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
22 epizódok
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