Software Misadventures
Emmanuel Ameisen - On production ML at Stripe scale, leading 100+ ML projects, iterating fast, and much more - #11
Jun 11, 2021
Emmanuel Ameisen, a machine learning engineer at Stripe and former lead at Insight Data Science, shares invaluable insights on building and deploying ML products at scale. He highlights common pitfalls in launching ML projects, emphasizing practicality over complexity. Emmanuel discusses the challenges of transitioning from research to engineering roles and the necessity of effective data management. He also touches on validating models in production, exploring testing methodologies, and shares his experience writing a book for engineers.
01:12:59
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Quick takeaways
- Data quality is critical for ML model effectiveness, necessitating regular reviews and cleaning to enhance performance and accuracy.
- Aligning ML project goals with business objectives prevents focus on vanity metrics, ensuring that efforts translate into real-world value for users.
Deep dives
The Importance of Data Quality in Machine Learning
Data quality plays a crucial role in the effectiveness of machine learning models. Regularly reviewing and cleaning data can lead to significant performance improvements, as outdated or irrelevant data often leads to suboptimal modeling outcomes. For instance, removing unfiltered log events can drastically enhance model accuracy, showcasing that data management directly impacts business applications. This highlights the necessity for data scientists to prioritize data curation as part of their project workflow to unlock the full potential of their models.
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