AI/ ML Consumption Models - How to leverage AI/ ML for your Enterprise...
summary
Compares AI/ML consumption to cloud’s IaaS/PaaS/SaaS spectrum, claiming most enterprises should start right of “build” unless the use case demands full control; shows an AI plane (application, data, control) and maps options from cognitive APIs through managed platforms to raw frameworks like TensorFlow, citing providers such as Amazon SageMaker and Google AutoML. Notes skill gaps and that ~50% of experiments fail, forecasts $98 billion by 2023, and prescribes three B's (business first, buy vs build, build) and stack/partnership rules.
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chapters (8)
- 0:00Start
- 6:47Agenda
- 13:37Infrastructure Consumption Models
- 20:17AI/ML Consumption Models
- 26:44How to select the right model?
- 29:26Al Workloads - Classification
- 31:25Al Workloads placement
- 37:54Recommendations
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