How to Deploy a Machine Learning Model to Google Cloud for 20% Software Engineers (CS329s tutorial)
It's time to reveal the magician's secrets behind deploying machine learning models! In this tutorial, I go through an example machine learning deployment scenario using Google Cloud and an image recognition app called Food Vision 🍔👁.
Get all the 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 e…
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Chapters (24)
Intro/hello
1:42
Presentation start (what we’re going to cover)
6:00
Food Vision 🍔👁 (the app we’re building) recipe
11:16
The end goal we’re 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
Deploying a model on the cloud outline
21:14
Steps we’re going to go through to deploy our app
27:06
Question: “How do you identify hard samples in your data?”
37:53
Creating a bucket on Google Storage
45:51
Uploading to Google Storage from Google Colab
48:02
Deploying a model to AI Platform
52:50
Creating an AI Platform Prediction version
58:10
Creating 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’ll run into when deploying your models
1:20:12
Extensions you could perform on this tutorial
1:20:49
Part 2 start (tutorial overtime)
1:28:43
Dealing with different data shapes
1:32:35
An error you might run into when using the example app (3 total models deploye
1:33:20
Dealing with data size restrictions
1:38:48
Stepping through the make gcloud-deploy command
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