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ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

Released Wednesday, 1st February 2023
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ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

Wednesday, 1st February 2023
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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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