From c82e7cca7b3615ca3fb2d0c848d0d0f59214aa17 Mon Sep 17 00:00:00 2001 From: Giorgio Conte Date: Mon, 6 Mar 2023 11:12:36 +0000 Subject: [PATCH] added demo README file --- .../data-solutions/bq-ml/demo/README.md | 38 +++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 blueprints/data-solutions/bq-ml/demo/README.md diff --git a/blueprints/data-solutions/bq-ml/demo/README.md b/blueprints/data-solutions/bq-ml/demo/README.md new file mode 100644 index 000000000..2b5af642d --- /dev/null +++ b/blueprints/data-solutions/bq-ml/demo/README.md @@ -0,0 +1,38 @@ +# BigQuery ML and Vertex AI Pipeline Demo + +This demo shows how to combine BigQuery ML (BQML) and Vertex AI to create a ML pipeline leveraging the infrastructure created in the blueprint. + +More in details, this tutorial will focus on the following three steps: + +- define a Vertex AI pipeline to create features, train and evaluate BQML models +- serve a BQ model through an API powered by Vertex AI Endpoint +- create batch prediction via BigQuery + +# Dataset + +This tutorial uses a fictitious e-commerce dataset collecting programmatically generated data from the fictitious e-commerce store called The Look. The dataset is publicy available on BigQuery at this location `bigquery-public-data.thelook_ecommerce`. + +# Goal + +The goal of this tutorial is to train a classification ML model using BigQuery ML and predict if a new web session is going to convert. + +The tutorial focuses more on how to combine Vertex AI and BigQuery ML to create a model that can be used both for near-real time and batch predictions rather than the design of the model itself. + +# Main components + +In this tutorial we will make use of the following main components: +- Big Query: + - standard: to create a view which contains the model features and the target variable + - ML: to train, evaluate and make batch predictions +- Vertex AI: + - Pipeline: to define a configurable and re-usable set of steps to train and evaluate a BQML model + - Experiment: to keep track of all the trainings done via the Pipeline + - Model Registry: to keep track of the trained versions of a specific model + - Endpoint: to serve the model via API + - Workbench: to run this demo + +# How to get started + +1. Access the Vertex AI Workbench +2. clone this repository +2. run the [`bmql_pipeline.ipynb`](bmql_pipeline.ipynb) Jupyter Notebook \ No newline at end of file