Custom Model Training & Deployment on Google Cloud: A Vertex AI Guide in Go! | AI Heroes 2023 Talk

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⏩ Chapter:
00:00 intro
03:35 What if data and model are user-specific?
05:22 The use-case scenario: sleep quality prediction
06:55 A cloud based solution… in GO
09:35 Preliminary step - The task & data format
11:19 Step 1: Custom training script & model definition
13:58 Train script
18:23 Customer Docker file
19:36 Step 2: Go Client. Vertex AI interaction pattern
20:17 Create the bucket
20:47 Upload to the bucket
21:19 Step 3: Custom Training Job
24:22 Outcome - we can’t use it
24:57 Step 4.1: Upload to the registry
26:09 Step 4.2: The model deployment: endpoint
27:53 The final result - use it!

In this tutorial, we dive into custom model training and deployment on Google Cloud using Vertex AI with the Go programming language. Learn how to create a custom regression model using TensorFlow Decision Forests (TF-DF), containerize it with Docker, and fully control the deployment process from model setup to production. This video explores each step, from defining your custom training script to deploying the trained model as an endpoint on Google Cloud, all without relying on the AutoML interface. Follow along with Paolo Galeone, Tech Lead at Zuru Tech, for a deep dive into custom model creation and deployment using the Vertex AI Go client, giving you maximum control over your machine learning pipeline and making your deployment as cost-effective as possible. Whether you're experienced with machine learning or just starting, this talk provides invaluable guidance.
🎤 Paolo Galeone, Tech Lead, Zuru Tech
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