Course Overview
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.
Who should attend
Introductory-level training for researchers wanting to use Google Cloud for ingesting, managing 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 versus unsupervised models
Course 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
Outline: Google Cloud Fundamentals for Researchers (GCFR)
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