Upleveling Data Labeling with Sama’s Jerome Pasquero

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.