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+# 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
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