Scaling the future of Machine Learning in enterprises
summary
Predicts enterprises reach millions of models deployed within roughly five to ten years and argues the bottleneck shifts from model creation to systems that operationalize and monitor them: templated pipelines, QA/QC, continuous performance tracking and governance. Expects workforce growth into tens of thousands of contributors (not all PhDs), widespread deployment across assets and factories, rapid model churn that requires automated retraining/retirement, and measurable ROI only when deployment, people, and responsible AI practices are all in place.
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chapters (5)
- 0:00Introduction
- 4:36The three dimensions of scale
- 7:04The effect on employees
- 8:44Inclusion
- 9:21Getting started
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