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#259 Larysa Visengeriyeva from mind scripts and biases to MLOps

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Contenuto fornito da Timothée Bourguignon. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Timothée Bourguignon o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

Larysa placed the start of her journey in the country she was born in, the Soviet Union, with her discovery of those books filled with 0s and 1s that she could not understand. Fast forward a few years, and she is hooked by Maths and Computer Science and doesn't want to do anything else. After her Master's degree in Odessa, she moved to Germany and studied again. From then on, we talked about how she pursued a Ph.D., pivoted on the problem she was tackling, and discovered a new field ML-Ops. We discussed the state of ML-Ops, how it is placed in the "Data" and ML problem chain, and how one should go at it.
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297 episodi

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Manage episode 367129868 series 2390187
Contenuto fornito da Timothée Bourguignon. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Timothée Bourguignon o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

Larysa placed the start of her journey in the country she was born in, the Soviet Union, with her discovery of those books filled with 0s and 1s that she could not understand. Fast forward a few years, and she is hooked by Maths and Computer Science and doesn't want to do anything else. After her Master's degree in Odessa, she moved to Germany and studied again. From then on, we talked about how she pursued a Ph.D., pivoted on the problem she was tackling, and discovered a new field ML-Ops. We discussed the state of ML-Ops, how it is placed in the "Data" and ML problem chain, and how one should go at it.
Here are the links from the show

Credits

Support the show

  continue reading

297 episodi

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