Google Cloud Fundamentals for Researchers
Contact us to book this course
Learning Track
Data Engineering and Analytics
Delivery methods
On-Site, Virtual
Duration
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.
Learning Objectives
- Understand products available in Google Cloud for research
- Load unstructured and structured data into Google Cloud
- Manage access and sharing your data on Google Cloud
- Understand costs on Google Cloud
- Leverage Jupyter Notebook environments in Vertex AI Workbench
- Utilize machine learning solutions on Google Cloud
Prerequisites
- Basic knowledge of data types and SQL
- Basic programming knowledge
- Machine learning models such as supervised versus unsupervised models
Course outline
- 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
- Organizing resources in Google Cloud
- Controlling Access to projects and resources
- Cost and billing management
- Interacting with Google Cloud
- Create and Manage Cloud Storage Buckets
- Compute Engine virtual machines
- Understanding computing costs
- Introduction to HPC on Google Cloud
- Lab: Create and Manage a Virtual Machine (Linux) and Cloud Storage
- Optional Lab: Deploy an HPC Cluster with Slurm
- BigQuery fundamentals
- Querying public datasets
- Importing and exporting data in BigQuery
- Connecting to Looker Studio
- Lab: BigQuery and Looker Studio Fundamentals
- Vertex AI
- Vertex AI Workbench
- Connecting Jupyter notebooks to BigQuery
- Lab: Interacting with BigQuery using Python and R Running in Jupyter Notebooks
- ML Options on Google Cloud
- Prebuilt ML APIs
- Vertex AI AutoML
- BigQuery ML
- Optional Lab: Extract, Analyze, and Translate Text from Images with the Cloud ML APIs
- Optional Lab: Identify Damaged Car Parts with Vertex AutoML Vision
- Optional Lab: Getting Started with BigQuery Machine Learning
