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Upleveling Data Labeling with Sama’s Jerome Pasquero

Upleveling Data Labeling with Sama’s Jerome Pasquero

As a Senior Product Manager at Sama, Jerome Pasquero understands the power of data, and he joins us today to share a wealth of knowledge on how better annotation ensures better models.

Key Points From This Episode:

  • Jerome’s background, interest in AI, and how he landed his role at Sama.
  • Social initiatives, training data, and what attracted Jerome to Sama.
  • The shift from focusing on AI models to the importance of data quality.
  • Why academia requires the use of a foundational dataset to compare models.
  • The reason for the early focus on building new AI models.
  • Whether datasets will become open source in the future as models have.
  • The role of annotation in making data meaningful and useful.
  • Challenges of annotating data and different approaches to doing so.
  • The three components of data annotation: models, filtering, and the annotation pipeline.
  • How to hone in on goals for filtering data into valuable subsets that align with your desired outcomes.
  • How to measure a model’s accuracy by focusing on user experience and more.
  • What data drift is and how to prevent it by keeping track of it and retraining models where necessary.
  • How to know that your training data is close enough to your production data.
  • What excites Jerome most about the world of data and annotation.

Stream the full episode below, or head here to select your favorite listening app and view the full transcript.

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