Simplify and accelerate AI for the entire data science team
summary
Claims that enterprises accelerate ML by broadening who builds models beyond data scientists to data engineers and BI analysts, by mixing local and multi cloud compute, and by supporting PyTorch TensorFlow scikit learn with ONNX and Intel/NVIDIA hardware. Recommends a understand control protect trust stack using explainability and data sheets, identifies two repeatable patterns augmenting analytics and ML-powered apps, and gives retail and farming examples that produced measurable gains.
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chapters (16)
- 0:00Intro
- 1:10Why accelerate AI
- 3:50Assumptions
- 4:50People
- 6:30How to accelerate
- 8:20GPU support
- 8:40Local compute
- 10:20Deep learning frameworks
- 11:50Responsible AI
- 13:40Empowering people
- 15:20Patterns
- 17:00Extend ML Program
- 20:40MLPowered App
- 21:50ABB
- 22:35Summary
- 24:00Questions
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