Machine Learning in the age of increasing Data Privacy Consciousness
summary
Argues concrete costs and practices: cites 20 million or 4% of global revenue as GDPR fines and 87% identifiable from date of birth gender and zip as a risk. Describes pipelines built with PySpark and TensorFlow, a home-card correction UI, a chatbot recommendation flow that surfaces top features, and a fraud-analysis page exposing indicators. Recommends data minimization, anonymization, differential privacy, and progressive consent; shows these reduce exposure while preserving usable personalized products.
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chapters (5)
- 0:00Data privacy in ML
- 3:35Why privacy matters
- 14:43Privacy-conscious ML at Shopify
- 25:57Privacy versus innovation
- 30:56Conclusion and key takeaways
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