Google Cloud Fundamentals for Researchers (GCFR) – Outline

Detailed 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 notebooks to BigQuery
• Lab 4: Interacting with BigQuery using Python and R Running in Jupyter Notebooks

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