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In this course, you will learn how to use various tools in the Google Cloud to collect, manage and use your data to gain insights for your research. You will get an introduction to the tools used by researchers in the Google Cloud. You will then 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 use Notebook environments and other Google Cloud tools for descriptive and predictive analytics.
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Course Contents
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- Google Cloud demos for researchers
- Google project concepts
- Data processing and storage in Google Cloud
- BigQuery
- Vertex AI Notebooks
- Machine learning
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Target Group
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Basic training for researchers who want to use Google Cloud to collect, manage and use their data.
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Knowledge Prerequisites
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Knowledge in one or more of the following areas is recommended but not required:
- Basic knowledge of data types and SQL
- Basic programming knowledge
- Machine learning models such as supervised and unsupervised models
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Course Objective
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- Understanding the products available in Google Cloud for research
- Loading unstructured and structured data into Google Cloud
- Manage access and sharing of your data in Google Cloud
- Understand costs on Google Cloud
- Utilize Jupyter Notebook environments in Vertex AI Workbench
- Utilize machine learning solutions on Google Cloud
| Google Cloud demos for Researchers |
| Demo: Provisioning Compute Engine virtual machines |
| Demo: Querying a billion rows of data in seconds with BigQuery |
| Demo: Training a custom image classification model with AutoML Vision |
| Google Project Concepts |
| Organizing resources in Google Cloud |
| Controlling access to projects and resources |
| Cost and billing management |
| 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 |
| Exercise: Creating and managing a virtual machine (Linux) and cloud storage |
| BigQuery |
| BigQuery fundamentals |
| Querying public datasets |
| Importing and exporting data in BigQuery |
| Connecting to Looker Studio |
| Exercise: BigQuery and Looker Studio basics |
| Notebooks on Vertex AI |
| Sharing APIs and services |
| Vertex AI |
| Vertex Workbench |
| Connecting Jupyter Notebooks to BigQuery |
| Exercise: Interacting with BigQuery using Python and R in Jupyter Notebooks |
| Machine learning |
| ML options on Google Cloud |
| Prebuilt ML APIs |
| Vertex AI AutoML |
| BigQuery ML |
| Exercise: Optional exercises (takeaway) to choose from: |
| Extracting, analyzing, and translating text from images with Cloud ML APIs |
| Identifying damaged car parts with Vertex AutoML Vision |
| Getting started with BigQuery Machine Learning |
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Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
In this course, you will learn how to use various tools in the Google Cloud to collect, manage and use your data to gain insights for your research. You will get an introduction to the tools used by researchers in the Google Cloud. You will then 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 use Notebook environments and other Google Cloud tools for descriptive and predictive analytics.
-
Course Contents
-
- Google Cloud demos for researchers
- Google project concepts
- Data processing and storage in Google Cloud
- BigQuery
- Vertex AI Notebooks
- Machine learning
-
Target Group
-
Basic training for researchers who want to use Google Cloud to collect, manage and use their data.
-
Knowledge Prerequisites
-
Knowledge in one or more of the following areas is recommended but not required:
- Basic knowledge of data types and SQL
- Basic programming knowledge
- Machine learning models such as supervised and unsupervised models
-
Course Objective
-
- Understanding the products available in Google Cloud for research
- Loading unstructured and structured data into Google Cloud
- Manage access and sharing of your data in Google Cloud
- Understand costs on Google Cloud
- Utilize Jupyter Notebook environments in Vertex AI Workbench
- Utilize machine learning solutions on Google Cloud
| Google Cloud demos for Researchers |
| Demo: Provisioning Compute Engine virtual machines |
| Demo: Querying a billion rows of data in seconds with BigQuery |
| Demo: Training a custom image classification model with AutoML Vision |
| Google Project Concepts |
| Organizing resources in Google Cloud |
| Controlling access to projects and resources |
| Cost and billing management |
| 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 |
| Exercise: Creating and managing a virtual machine (Linux) and cloud storage |
| BigQuery |
| BigQuery fundamentals |
| Querying public datasets |
| Importing and exporting data in BigQuery |
| Connecting to Looker Studio |
| Exercise: BigQuery and Looker Studio basics |
| Notebooks on Vertex AI |
| Sharing APIs and services |
| Vertex AI |
| Vertex Workbench |
| Connecting Jupyter Notebooks to BigQuery |
| Exercise: Interacting with BigQuery using Python and R in Jupyter Notebooks |
| Machine learning |
| ML options on Google Cloud |
| Prebuilt ML APIs |
| Vertex AI AutoML |
| BigQuery ML |
| Exercise: Optional exercises (takeaway) to choose from: |
| Extracting, analyzing, and translating text from images with Cloud ML APIs |
| Identifying damaged car parts with Vertex AutoML Vision |
| Getting started with BigQuery Machine Learning |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.