GCFRGOGoogleGO-GCFR1.0<ul> <li>Understanding products available in Google Cloud for research</li><li>Loading unstructured and structured data into Google Cloud</li><li>Managing access and sharing your data on Google Cloud</li><li>Understandings costs on Google Cloud</li><li>Leveraging Jupyter Notebook environments in Vertex AI Workbench</li><li>Utilizing machine learning solutions on Google Cloud</li></ul><p>Understanding of one or more of the following is recommended, but not required: </p> <ul> <li>Basic knowledge of data types and SQL</li><li>Basic programming knowledge</li><li>Machine learning models such as supervised versus unsupervised models</li></ul><p>Introductory-level training for researchers wanting to use Google Cloud for ingesting, managing and leveraging their data.</p><h4>Module 1 - Google Cloud Demos for Researchers</h4><ul> <li>Demo: Provision Compute Engine virtual machines</li><li>Demo: Query a billion rows of data in seconds using BigQuery</li><li>Demo: Train a custom vision model using AutoML Vision</li></ul><h4>Module 2 - Google Project Concepts</h4><ul> <li>Organizing resources in Google Cloud</li><li>Controlling Access to projects and resources</li><li>Cost and billing management</li></ul><h4>Module 3 - Computing and Storage on Google Cloud</h4><ul> <li>Interacting with Google Cloud</li><li>Create and Manage Cloud Storage Buckets</li><li>Compute Engine virtual machines</li><li>Understanding computing costs</li><li>Introduction to HPC on Google Cloud</li><li>Lab 1: Create and Manage a Virtual Machine (Linux) and Cloud Storage</li></ul><h4>Module 4 - BigQuery</h4><ul> <li>BigQuery fundamentals</li><li>Querying public datasets</li><li>Importing and exporting data in BigQuery</li><li>Connecting to Looker Studio</li><li>Lab 3: BigQuery and Looker Studio Fundamentals</li></ul><h4>Module 5 - Vertex AI Notebooks</h4><ul> <li>Enabling APIs and services</li><li>Vertex AI</li><li>Vertex Workbench</li><li>Connecting Jupyter notebooks to BigQuery</li><li>Lab 4: Interacting with BigQuery using Python and R Running in Jupyter Notebooks</li></ul><h4>Module 6 - Machine Learning</h4><ul> <li>Types of ML within Google Cloud</li><li>Prebuilt ML APIs</li><li>Vertex AI AutoML</li><li>BigQuery ML</li><li>Lab 5: Optional (take-home) labs to choose from:<ul> <li>Extract, Analyze, and Translate Text from Images with the Cloud ML APIs</li><li>Identify Damaged Car Parts with Vertex AutoML Vision</li><li>Getting Started with BigQuery Machine Learning</li></ul></li></ul>- 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 CloudUnderstanding 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 modelsIntroductory-level training for researchers wanting to use Google Cloud for ingesting, managing and leveraging their data.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 Learning1 day595.00660.00650.00820.00950.00950.00950.00790.00950.00