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Feature Platforms for Data-Centric AI with Mike Del Balso - #577

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Manage episode 330859614 series 2355587
Innehåll tillhandahållet av TWIML and Sam Charrington. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av TWIML and Sam Charrington eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

In the latest installment of our Data-Centric AI series, we’re joined by a friend of the show Mike Del Balso, Co-founder and CEO of Tecton. If you’ve heard any of our other conversations with Mike, you know we spend a lot of time discussing feature stores, or as he now refers to them, feature platforms. We explore the current complexity of data infrastructure broadly and how that has changed over the last five years, as well as the maturation of streaming data platforms. We discuss the wide vs deep paradox that exists around ML tooling, and the idea around the “ML Flywheel”, a strategy that leverages data to accelerate machine learning. Finally, we spend time discussing internal ML team construction, some of the challenges that organizations face when building their ML platforms teams, and how they can avoid the pitfalls as they arise.

The complete show notes for this episode can be found at twimlai.com/go/577

  continue reading

699 episoder

Artwork
iconDela
 
Manage episode 330859614 series 2355587
Innehåll tillhandahållet av TWIML and Sam Charrington. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av TWIML and Sam Charrington eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

In the latest installment of our Data-Centric AI series, we’re joined by a friend of the show Mike Del Balso, Co-founder and CEO of Tecton. If you’ve heard any of our other conversations with Mike, you know we spend a lot of time discussing feature stores, or as he now refers to them, feature platforms. We explore the current complexity of data infrastructure broadly and how that has changed over the last five years, as well as the maturation of streaming data platforms. We discuss the wide vs deep paradox that exists around ML tooling, and the idea around the “ML Flywheel”, a strategy that leverages data to accelerate machine learning. Finally, we spend time discussing internal ML team construction, some of the challenges that organizations face when building their ML platforms teams, and how they can avoid the pitfalls as they arise.

The complete show notes for this episode can be found at twimlai.com/go/577

  continue reading

699 episoder

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