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Get all code on GitHub – https://github.com/mrdbourke/cs329s-ml-deployment-tutorial
Slides – https://github.com/mrdbourke/cs329s-ml-deployment-tutorial/blob/main/CS329s-deploying-ml-models-tutorial.pdf
Full CS329s syllabus – https://stanford-cs329s.github.io/index.html
Learn ML (my beginner-friendly ML course) – https://dbourke.link/mlcourse
Connect elsewhere:
Web – https://www.mrdbourke.com
Receive email updates about my work – https://www.mrdbourke.com/newsletter
Timestamps:
0:00 – Intro/hello
1:42 – Start of the presentation (what we will cover)
6:00 – Food Vision (the app we are building) recipe
11:16 – The end goal we are working towards (data flywheel)
13:07 – The data flywheel: the holy grail of ML apps
14:57 – Tesla's data flywheel
17:02 – Food Vision's data flywheel
18:24 – Deploy a model to the cloud contour
21:14 – Steps we will go through to deploy our app
27:06 – Question: “How do you identify hard examples in your data?”
37:53 – Create a bucket on Google Storage
45:51 – Uploading to Google Storage from Google Colab
48:02 – Deploy a model on AI Platform
52:50 – Create an AI Platform Prediction version
58:10 – Create a service account to access our model on Google Cloud
1:02:32 – Authenticating our app with our private service account key
1:09:19 – What happens when we run make gcloud-deploy
1:11:27 – Problems you encounter when deploying your models
1:20:12 – Extensions you can run in this tutorial
1:20:49 – Start part 2 (overtime tutorial)
1:28:43 – Dealing with different data forms
1:32:35 – An error you might encounter when using the sample app (3 models deployed in total)
1:33:20 – Dealing with data size limitations
1:38:48 – Running the make gcloud-deploy command
1:51:00 – Summary and conclusion
#machinelearning
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