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4 changes: 3 additions & 1 deletion doc/for-presenters.md
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Expand Up @@ -6,6 +6,8 @@ This workshop is designed to show application developers how to incorporate mach
- how to evaluate and validate predictive models, and some metrics that are more useful than raw accuracy;
- how OpenShift enables every stage of machine learning discovery and production, including trying different approaches to solve a problem.

Slides to accompany this workshop can be found [here.](https://docs.google.com/presentation/d/1JYw867N-nPI3fQY6jBuzsjxt4qIKtMaewUfDwN9AR_k/edit?usp=sharing)

## Background and workshop flow

First, follow the instructions in [deploy.md](../deploy/deploy.md), installing the OpenShift template in a project for each workshop user (or just for yourself if you're trying it out locally). A more streamlined experience is coming soon!
Expand Down Expand Up @@ -66,4 +68,4 @@ There's one notebook left, and it's more or less "extra credit":

- `99-baseline.ipynb` shows the classic Naive Bayes spam classification technique, which formed the basis for the first successful automatic spam classifiers. Your attendees are unlikely to tune the other approaches so that they outperform Naive Bayes, but they may have fun trying!

The final section of the slides briefly introduces some other topics of interest: a call to action to get involved with some key open source communities (the Open Data Hub, radanalytics.io, and Kubeflow), how OpenShift facilitates GPU and CPU acceleration of linear algebra, and the promise of function-as-a-service for machine learning systems.
The final section of the slides briefly introduces some other topics of interest: a call to action to get involved with some key open source communities (the Open Data Hub, radanalytics.io, and Kubeflow), how OpenShift facilitates GPU and CPU acceleration of linear algebra, and the promise of function-as-a-service for machine learning systems.