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