An end-to-end workflow completely within the Vertex AI interface in the Google Cloud Console. We will use AutoML to train a machine learning model. A walkthrough of all the steps from connecting to data sources, training a model, evaluating the final model, deploying to an online endpoint and requesting predictions from multiple clients. A few deep dives along the way! This video follows the notebook 02a - Vertex AI - AutoML in GCP Console (no code).
GitHub Repository: https://github.com/statmike/vertex-ai-mlops
The Notebook followed in this video: https://github.com/statmike/vertex-ai-mlops/blob/main/02%20-%20Vertex%20AI%20AutoML/02a%20-%20Vertex%20AI%20-%20AutoML%20in%20GCP%20Console%20(no%20code).ipynb
Timeline:
0:00 - Introduction
3:25 - Overview
5:08 - Start Walkthrough
6:38 - Create Dataset
7:40 - Discuss Types of Model for AutoML
11:12 - Select Source of Data
12:50 - Train Model with AutoML
16:35 - Q&A: What optimization objective to choose?
23:08 - Set Budget and run training
24:22 - Evaluate Model
30:43 - Deploy & Test (Endpoint)
36:32 - Review Endpoint
39:50 - Online Predictions
47:42 - Online Model Explanations
48:33 - Q&A: What are explanations?
51:35 - Review Explanations for Model
53:23 - Batch Predictions
56:56 - Q&A: When should I use the Console for AutoML?
58:43 - Wrap-up