ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź
Podcast |
MLOps Live
Publisher |
neptune.ai
Media Type |
audio
Podknife tags |
Artificial Intelligence, 
Interview, 
Machine Learning, 
Technology
Categories Via RSS |
Technology
Publication Date |
Feb 01, 2023
Episode Duration |
00:54:43
In this episode of MLOps Live, Piotr is joined by Aurimas Griciūnas, a Senior Solutions Architect at neptune.ai. Aurimas elaborates on the distinctions between machine learning (ML) platforms and data platform teams and the advantages of automating an ML stack. He also went over the process normalization requirements for streamlined teams from data platform and ML platform groups. Aurimas explains why data platforms are primarily concerned with displaying results, whereas ML platforms are centered on the frameworks and tooling that machine learning professionals use on a daily basis.
In this episode of MLOps Live, Piotr is joined by Aurimas Griciūnas, a Senior Solutions Architect at neptune.ai. Aurimas elaborates on the distinctions between machine learning (ML) platforms and data platform teams and the advantages of automating an ML stack. He also went over the process normalization requirements for streamlined teams from data platform and ML platform groups. Aurimas explains why data platforms are primarily concerned with displaying results, whereas ML platforms are centered on the frameworks and tooling that machine learning professionals use on a daily basis.
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Previous guests include: Andy McMahon of NatWest Group, Jacopo Tagliabue of Coveo, Adam Sroka of Origami, Amber Roberts of Arize AI, Michal Tadeusiak of deepsense.ai, Danny Leybzon of WhyLabs, Kyle Morris of Banana ML, Federico Bianchi of Università Bocconi, Mateusz Opala of Brainly, Kuba Cieslik of tuul.ai, Adam Becker of Telepath.io and Fernando Rejon & Jakub Zavrel of Zeta Alpha Vector. Check out our three most downloaded episodes:
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