Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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Introduction to Analytics and AI
This modules talks about ML options on Google Cloud
  • Module introduction
  • What is AI?
  • From ad-hoc data analysis to data-driven decisions
  • Options for ML models on Google Cloud
  • QUIZ
Prebuilt ML Model APIs for Unstructured Data
This module focuses on using pre-built ML APIs on your unstructured data
  • Module introduction
  • Unstructured data is hard
  • ML APIs for enriching data
  • Lab Intro: Using the Natural Language API to Classify Unstructured Text
  • required
  • LAB: Using the Natural Language API to classify unstructured text: In this lab you’ll learn how to classify text into categories using the Natural Language API
  • QUIZ
Big Data Analytics with Notebooks
This module covers how to use Notebooks
  • Module introduction
  • What’s a Notebook?
  • BigQuery magic and ties to Pandas
  • Lab Intro: BigQuery in JupyterLab on Vertex AI
  • LAB: BigQuery in JupyterLab on Vertex AI 2.5: The purpose of this lab is to show learners how to instantiate a Jupyter notebook running on Google Cloud Platform's AI Platform service.
  • QUIZ
Production ML Pipelines
This module covers building custom ML models and introduces Vertex AI and TensorFlow Hub
  • Module introduction
  • Ways to do ML on Google Cloud
  • Vertex AI Pipelines
  • TensorFlow Hub
  • Lab Intro: Running Pipelines on Vertex AI
  • LAB: Running Pipelines on Vertex AI 2.5: In this lab, you learn how to utilize Vertex AI Pipelines to execute a simple Kubeflow Pipeline SDK derived ML Pipeline.
  • Summary
  • QUIZ
Custom Model Building with SQL in BigQuery ML
This module covers BigQuery ML
  • Module introduction
  • BigQuery ML for Quick Model Building
  • Supported models
  • Lab Intro: Predict Bike Trip Duration with a Regression Model in BigQuery ML 1 minute
  • LAB: Predict Bike Trip Duration with a Regression Model in BQML 2.5: In this lab you will use the London bicycles dataset to build a regression model in BQML to predict trip duration.
  • Lab Intro: Movie Recommendations in BigQuery ML
  • LAB: Movie Recommendations in BigQuery ML 2.5: In this lab you'll use the MovieLens dataset to build a collaborative filtering model and use it to make predictions.
  • Summary
  • QUIZ
Custom Model Building with AutoML
  • Module introduction
  • Why AutoML?
  • AutoML Vision
  • AutoML Natural Language Processing
  • AutoML Tables
  • Summary
  • QUIZ
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