5 Lessons Learned from Implementing 40+ Machine Learning Projects
summary
Inmeta reports running 40+ machine learning projects over four to five years with a 20+ person data science team and finds data preparation is the biggest time sink — more than model training — while data exploration/transformation and production deployment are the hardest steps. About 40% of startups claiming AI do no real ML, so clearly defined business objectives, measurable success criteria, data quality checks and an enterprise data and AI strategy determine whether models actually deliver value.
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chapters (7)
- 0:00Intro
- 0:26Who is Xiaopeng
- 1:13Why am I here
- 3:47What will I talk about
- 4:45Lessons Learned
- 12:33Data AI Strategy
- 14:22Summary
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