How to Deploy a Machine Learning Model to Google Cloud for 20% Software Engineers (CS329s tutorial)

Daniel Bourke · Beginner ·📰 AI News & Updates ·5y ago
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 elsewhere: Web - https://www.mrdbourke.com Get email updates on my work - https://www.mrdbourke.com/newsletter Timestamps: 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 - S
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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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