Intelligent big machines that shock even engineers.
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 [More]