Course
digicode: GCFR
Google Cloud Fundamentals for Researchers
Learn how to use various tools in Google Cloud to ingest, manage and leverage your data to derive insights in your research.
Duration
1 day
Price
850.–
exkl. 8.1% MWST
Course documents
Official Google Cloud courseware
Course facts
- 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
- Understanding costs on Google Cloud
- Leveraging Jupyter Notebook environments in Vertex AI Workbench
- Utilizing machine learning solutions on Google Cloud
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.
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
- Exploring research use cases in Google Cloud through interactive demos
2 Google Cloud Project Concepts
- Organizing resources in Google Cloud
- Controlling Access to projects and resources
- Cost and billing management
- Understanding how resources in Google Cloud are managed across organizations, folders and projects
- Controlling access to projects and resources using IAM
- Exploring billing in Google Cloud
3 Computing and Storage in Google Cloud
- Interacting with Google Cloud
- Creating and Managing Cloud Storage Buckets
- Compute Engine virtual machines
- Understanding computing costs
- Introduction to HPC on Google Cloud
- Understanding the methods of interacting with Google Cloud
- Storing your data in Cloud Storage buckets
- Provisioning Compute Engine virtual machines
- Understanding computing costs on Google Cloud
- Exploring how you can create HPC clusters on Google Cloud
- Lab: Creating and Managing a Virtual Machine (Linux) and Cloud Storage
- Optional Lab: Deploy an HPC Cluster with Slurm
4 BigQuery
- BigQuery fundamentals
- Querying public datasets
- Importing and exporting data in BigQuery
- Connecting to Looker Studio
- Understanding the fundamentals of BigQuery
- Querying public datasets in BigQuery Studio
- Managing datasets in BigQuery
- Connecting data in BigQuery to Looker Studio
- Lab: BigQuery and Looker Studio Fundamentals
5 Notebooks on Vertex AI
- Vertex AI
- Vertex AI Workbench
- Connecting Jupyter notebooks to BigQuery
- Exploring Vertex AI as a machine learning platform
- Provisioning Jupyter notebooks using Vertex AI Workbench
- Lab: Interacting with BigQuery using Python and R Running in Jupyter Notebooks
6 Machine Learning on Google Cloud
- ML Options on Google Cloud
- Prebuilt ML APIs
- Vertex AI AutoML
- BigQuery ML
- Exploring machine learning options on Google Cloud
- Understanding unstructured data using prebuilt ML APIs
- Creating no-code custom ML models using Vertex AI AutoML
- Creating custom ML models using SQL on 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
- Basic knowledge of data types and SQL
- Basic programming knowledge
- Machine learning models such as supervised versus unsupervised models
Products:
- Compute Engine
- Cloud Storage
- BigQuery
- Looker Studio
- Vertex AI Workbench
- Vertex AI AutoML