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S2 E2: Lina Weichbrodt, DKB: AI diagnostics, usability and post mortems

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Indhold leveret af Silo AI. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Silo AI eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

S2 E2: Lina Weichbrodt of Deutsche Kreditbank discusses AI diagnostics, usability and post mortems.

Lina Weichbrodt is the Machine Learning Lead Engineer at Deutsche Kreditbank, formerly at Zalando. She has pioneered Machine Learning -focused quality management, debugging and post mortem practices throughout her career and shares her insights regarding ML quality & problem discovery. Machine Learning quality monitoring can be notoriously difficult, due to an absence of the “it either works or doesn’t” -logic common in traditional software applications.

Tune in to catch Lina’s recommendations on which PeopleOps principles have a strong indirect affect on algorithmic quality, how ML quality ownership should be allocated, and tips for the best toolkits & recommendations for product owners on how ML quality should be approached.

#aiforpeople #inferencepodcast #siloai

  continue reading

30 episoder

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Manage episode 327928122 series 3248802
Indhold leveret af Silo AI. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Silo AI eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

S2 E2: Lina Weichbrodt of Deutsche Kreditbank discusses AI diagnostics, usability and post mortems.

Lina Weichbrodt is the Machine Learning Lead Engineer at Deutsche Kreditbank, formerly at Zalando. She has pioneered Machine Learning -focused quality management, debugging and post mortem practices throughout her career and shares her insights regarding ML quality & problem discovery. Machine Learning quality monitoring can be notoriously difficult, due to an absence of the “it either works or doesn’t” -logic common in traditional software applications.

Tune in to catch Lina’s recommendations on which PeopleOps principles have a strong indirect affect on algorithmic quality, how ML quality ownership should be allocated, and tips for the best toolkits & recommendations for product owners on how ML quality should be approached.

#aiforpeople #inferencepodcast #siloai

  continue reading

30 episoder

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