End-To-End: ML with SQL in BigQuery (BQML) [notebook 03a]

An end-to-end workflow using the Python client for BigQuery on Google Cloud Platform. We use BigQuery ML to train a model using SQL! A walkthrough of all the steps from connecting to data sources, training a model, evaluating the final model, and requesting predictions from multiple clients. A few deep dives along the way including model explainability! This video follows the notebook 03a - BigQuery Machine Learning (BQML) - Machine Learning with SQL.

GitHub Repository: https://github.com/statmike/vertex-ai...

The Notebook followed in this video is an older version - link for the version in the video: https://github.com/statmike/vertex-ai...

An updated version of the notebook can be found here: https://github.com/statmike/vertex-ai...

BigQuery Overview in Previous Video:    • Data Source - Vertex AI for ML Operat...  

Timeline:
0:00 - Introduction
0:51 - Overview
4:03 - Start Walkthrough
7:10 - [notebook section] Setup
8:57 - [notebook section] Train Model
20:40 - [notebook section] Evaluate Model (with console first)
29:08 - [notebook section] Evaluate Model (with code)
33:55 - [notebook section] Predictions
38:29 - [notebook section] Explanations
44:51 - Q&A: Why use BigQuery for ML?
46:27 - Q&A: What did you use Python clients for BigQuery?
47:37 - Q&A: What types of models can BigQuery ML train?
51:02 - Q&A: What if I want online predictions?
52:32 - Wrap-up