The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349
9 snips
Feb 17, 2020 Emmanuel Ameisen, a machine learning engineer at Stripe and author of "Building Machine Learning Powered Applications," dives deep into the journey of turning ideas into ML products. He shares insights on structuring end-to-end projects and stresses the importance of explainability for model success. The conversation covers practical approaches to debugging, ethical considerations in deployment, and the necessity of post-deployment monitoring. Ameisen also emphasizes user feedback's role in refining ML applications, advocating for flexible development practices.
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Initial Idea
- Emmanuel Ameisen initially considered a fact-checking application for the book.
- Monica Rigotti advised against it due to potential harm and subjective opinions.
Workflow over training
- Focus on the end-to-end ML project workflow, not just model training.
- Prioritize building a minimum viable product (MVP) before deep research dives.
Data Exploration
- For any data project, spend at least a few hours examining the data.
- Never spend less time, ensuring comprehensive understanding before proceeding.
