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Data Science Model Life Cycle Explained #31

Closed hemajv closed 1 year ago

hemajv commented 2 years ago

Overview Connect with the women data scientists and software engineers at Red Hat who are leading the way on developing and designing Red Hat’s framework for managed services for use within the data science model life cycle. Within that framework, there are actually quite a few players/personas involved. We'll look at the people involved in this ML framework and the major stages of managed services that are required for model delivery. Each panelist will discuss and demo the basics of the stage of the data science life cycle that they are experts in and answer questions. Topics to be covered include: • Gathering/preparing data, • developing a ML model, • deploying a ML model with ML Ops Pipelines, and • monitoring and managing model performance. Along the way, we'll discuss various data services (Jupyter, Seldon Deploy, Starburst Galaxy) and highlight the importance of ML accelerators (NVIDIA GPUs) for the Red Hat OpenShift Data Science platform.

Speakers Diane Mueller @hemajv @isabelizimm @oindrillac @dfeddema @heyselbi @aakankshaduggal

What conference(s) are you submitting this proposal to? NVIDIA GTC (#30) , #51, #53

Project repo link, or other relevant resources NA

Link to abstract https://docs.google.com/document/d/1xIl2U2oVgExnsBaI6GuMza6yphn-fP_7dkv_KHVB2Pk/edit?usp=sharing

Was this proposal accepted?

oindrillac commented 2 years ago

Talk completed at #30 On demand portal: https://reg.rainfocus.com/flow/nvidia/nvidiagtc/ap2/page/sessioncatalog/session/1631907700052001y2sB

oindrillac commented 2 years ago

Stats on the Nvidia session: SIMULIVE: A31663 - Data Science Model Life Cycle Explained had 197 total views (125 live) 💻

oindrillac commented 2 years ago

Summit Abstract: https://docs.google.com/document/d/1x4AH-d1jFuRcOxBRg9Eq3CcVnNS5ILb2M6zsTJQboFw/edit

oindrillac commented 2 years ago

Accepted at Red Hat Summit #53

sesheta commented 2 years ago

Issues go stale after 90d of inactivity. Mark the issue as fresh with /remove-lifecycle stale. Stale issues rot after an additional 30d of inactivity and eventually close.

If this issue is safe to close now please do so with /close.

/lifecycle stale

sesheta commented 2 years ago

Stale issues rot after 30d of inactivity. Mark the issue as fresh with /remove-lifecycle rotten. Rotten issues close after an additional 30d of inactivity.

If this issue is safe to close now please do so with /close.

/lifecycle rotten

sesheta commented 1 year ago

Rotten issues close after 30d of inactivity. Reopen the issue with /reopen. Mark the issue as fresh with /remove-lifecycle rotten.

/close

sesheta commented 1 year ago

@sesheta: Closing this issue.

In response to [this](https://github.com/AICoE/content-pipeline/issues/31#issuecomment-1200361001): >Rotten issues close after 30d of inactivity. >Reopen the issue with `/reopen`. >Mark the issue as fresh with `/remove-lifecycle rotten`. > >/close Instructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md). If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes/test-infra](https://github.com/kubernetes/test-infra/issues/new?title=Prow%20issue:) repository.