Google Cloud Fundamentals for Researchers

(1 day)

 

In this course, you will learn how to use various tools in Google Cloud to ingest, manage, and leverage your data to derive insights in your research. You will be introduced to tools used on Google Cloud by researchers, then you will learn how to ingest your unstructured and structured data into Cloud Storage and BigQuery, respectively. Next, you will learn how to curate your data and understand costs in Google Cloud. Finally, you will learn how to leverage notebook environments and other Google Cloud tools for descriptive and predictive analysis.

Objectives

  • Understanding products available in Google Cloud for research
  • Loading unstructured and structured data into Google Cloud
  • Managing access and sharing your data on Google Cloud
  • Understandings costs on Google Cloud
  • Leveraging Jupyter Notebook environments in Vertex AI Workbench
  • Utilizing machine learning solutions on Google Cloud

Audience 

Introductory-level training for researchers wanting to use Google Cloud
for ingesting, curating, sharing, and leveraging their data.

Prerequisites

Understanding of one or more of the following is recommended, but
not required:

  • Basic knowledge of data types and SQL
  • Basic programming knowledge
  • Machine learning models such as supervised vs. unsupervised models

Course Outline

Module 1: Google Cloud Demos for Researchers

  • Demo: Provision Compute Engine Virtual Machines
  • Demo: Query a Billion Rows of Data in Seconds Using BigQuery
  • Demo: Train a Custom Vision Model Using AutoML Vision

Module 2: Google Project Concepts

  • Organizing resources in Google Cloud
  • Controlling access to projects and resources
  • Cost and billing management

Module 3: Computing and Storage on Google Cloud

  • Interacting with Google Cloud
  • Create and manage Cloud Storage Buckets
  • Compute Engine virtual machines
  • Understanding computing costs
  • Introduction to HPC on Google Cloud
  • Lab 1: Create and Manage a Virtual Machine (Linux) and Cloud Storage

Module 4: BigQuery

  • BigQuery fundamentals
  • Querying public datasets
  • Importing and exporting data in BigQuery
  • Connecting to Looker Studio
  • Lab 3: BigQuery and Looker Studio Fundamentals

Module 5: Vertex AI Notebooks

  • Enabling APIs and services
  • Vertex AI
  • Vertex Workbench
  • Connecting Jupyter Notebook to BigQuery
  • Lab 4: Interacting with BigQuery Using Python and R Running in Jupyter Notebook

Module 6: Machine Learning

  • Types of ML within Google Cloud
  • Prebuilt ML APIs
  • Vertex AI AutoML
  • BigQuery ML
  • Lab 5: Optional (take-home) labs to choose from:
    • Extract, Analyze, and Translate Text from Images with the Cloud ML APIs
    • Identify Damaged Car Parts with Vertex AutoML Vision
    • Getting Started with BigQuery Machine Learning