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 🍔👁.

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0:00 – 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 deployed)
1:33:20 – Dealing with data size restrictions
1:38:48 – Stepping through the make gcloud-deploy command
1:51:00 – Summary and wrap up


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