<commit_before>{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "okkqa_U8AcsN"
   },
   "source": [
    "# Experimenting BQML and AutoML with Vertex AI\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook demonstrates how to use Vertex AI Pipelines to rapid prototype a model using both AutoML and BQML, do an evaluation comparison, for a baseline, before progressing to a custom model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k-82uiXlTjvw",
    "tags": []
   },
   "source": [
    "### Learning objectives\n",
    "\n",
    "In this notebook, you learn how to use `Vertex AI Predictions` for rapid prototyping a model.\n",
    "\n",
    "This notebook uses the following Google Cloud ML services:\n",
    "\n",
    "- `Vertex AI Pipelines`\n",
    "- `Vertex AI AutoML`\n",
    "- `Vertex AI BigQuery ML`\n",
    "- `Google Cloud Pipeline Components`\n",
    "\n",
    "The steps performed include:\n",
    "\n",
    "- Create a BigQuery and Vertex AI training dataset.\n",
    "- Train a BigQuery ML and AutoML model.\n",
    "- Extract evaluation metrics from the BigQueryML and AutoML models.\n",
    "- Select and deploy the best trained model.\n",
    "- Test the deployed model infrastructure.\n",
    "- Run the pipeline.\n",
    "\n",
    "Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](../solutions/rapid_prototyping_bqml_automl.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl_and_bqml.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},<commit_msg>- Updated notebook path in solution folder.<commit_after>{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "okkqa_U8AcsN"
   },
   "source": [
    "# Experimenting BQML and AutoML with Vertex AI\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook demonstrates how to use Vertex AI Pipelines to rapid prototype a model using both AutoML and BQML, do an evaluation comparison, for a baseline, before progressing to a custom model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k-82uiXlTjvw",
    "tags": []
   },
   "source": [
    "### Learning objectives\n",
    "\n",
    "In this notebook, you learn how to use `Vertex AI Predictions` for rapid prototyping a model.\n",
    "\n",
    "This notebook uses the following Google Cloud ML services:\n",
    "\n",
    "- `Vertex AI Pipelines`\n",
    "- `Vertex AI AutoML`\n",
    "- `Vertex AI BigQuery ML`\n",
    "- `Google Cloud Pipeline Components`\n",
    "\n",
    "The steps performed include:\n",
    "\n",
    "- Create a BigQuery and Vertex AI training dataset.\n",
    "- Train a BigQuery ML and AutoML model.\n",
    "- Extract evaluation metrics from the BigQueryML and AutoML models.\n",
    "- Select and deploy the best trained model.\n",
    "- Test the deployed model infrastructure.\n",
    "- Run the pipeline.\n",
    "\n",
    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](../labs/rapid_prototyping_bqml_automl.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl_and_bqml.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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