F23 IN4023-G1 Virtual Systems Admin Lab 2 Google Cloud Service and how to become a Google Cloud Architect

What you are to do for Lab 2: December 14

How to hand in your work:
In this lab, we will explore and investigate what cloud application development means.
How you become a cloud application developer?
What job opportunities are available once you are a Certified Google Application Developer?
What tools, systems, processes, skills are needed to become a Cloud Application Developer?
What is the process of writing the exam and how to register and then write the Exam?
What can you start doing today to start the process?
Your hand in for this lab
Write a Blog Article : post it on LINKEDIN.
Send me the url of your Blog Article: put it into a text file: Named as your studentname_studentid.txt

You can create a notebook in the context of Google Cloud Services to guide your students through a self-paced lesson in using R for learning about AI and machine learning (ML) model building. Google Cloud's AI Platform Notebooks can be a suitable environment for this purpose.

Here's how you can set it up and structure the lesson:

Setting Up an AI Platform Notebook:
Start by creating a new AI Platform Notebook instance in Google Cloud.
Choose an environment that supports R. Google Cloud provides various images with pre-installed languages and frameworks. If an R-specific image is not available, you can choose a base image and install R manually.
Customizing the Notebook Environment:
Once the notebook instance is ready, customize the environment by installing necessary R packages and libraries related to AI and ML, such as caret, tidyverse, randomForest, keras, and others that are relevant to your lessons.
Creating the Lesson Content:
Develop a series of Jupyter notebooks in R that guide students through the concepts you want to teach.
Start with basic R programming and data structure concepts, then progressively introduce AI and ML topics.
Include practical exercises and examples in each notebook. For example, start with simple linear regression models and gradually move to more complex algorithms.
Incorporating Interactive Elements:
Use Jupyter notebook's interactive features like widgets to make the lessons more engaging.
Incorporate data visualization using libraries like ggplot2 to help students understand the data and model outputs better.
Adding Self-Paced Learning Components:
Structure the notebooks to allow self-paced learning. This can be done by clearly segmenting sections, providing detailed explanations, and including step-by-step instructions.
Include quizzes or interactive exercises at the end of each section for self-assessment.
Integrating Cloud Resources:
Utilize Google Cloud resources like BigQuery for handling large datasets, or Google Cloud Storage for storing and retrieving data.
Teach students how to access these resources directly from their notebooks.
Sharing and Access Control:
Once your notebooks are ready, share them with your students. Google Cloud allows you to control access, ensuring only your students can view and interact with the notebooks.
Continuous Updates and Feedback:
Regularly update the content based on new developments in AI and ML.
Encourage feedback from students and make improvements to the lessons accordingly.
By using Google Cloud's AI Platform Notebooks, you can provide a powerful and flexible learning environment for your students, enabling them to learn and experiment with AI and ML in R at their own pace.

what is Notebook in the context of Google Cloud services

In the context of Google Cloud services, a "Notebook" usually refers to a type of interactive computing environment that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. Specifically, within Google Cloud, this is often related to AI Platform Notebooks, which are a managed service that provides JupyterLab notebooks.
Key aspects of Google Cloud's AI Platform Notebooks include:
JupyterLab Environment: It offers a JupyterLab interface, which is a web-based interactive development environment for Jupyter notebooks, code, and data.
Integrated Machine Learning and Data Science Tools: These notebooks are pre-installed with machine learning and data science frameworks like TensorFlow, PyTorch, and scikit-learn, making it easier to start developing ML models.
Google Cloud Integration: The notebooks are integrated with Google Cloud services, allowing easy access to data stored in Google Cloud Storage, BigQuery, and other GCP services.
Scalability and Flexibility: Users can choose from various machine types to scale their resources according to the needs of their projects. They can also install additional packages and customize their environment.
Collaboration and Sharing: Similar to other notebook technologies, Google Cloud Notebooks can be easily shared among team members, promoting collaborative work on projects.
Security and Management: Google Cloud provides managed services with built-in security features, ensuring that the data and models are protected.
These notebooks are particularly popular among data scientists and machine learning engineers for exploratory data analysis, model development, data visualization, and creating reproducible research documents.


Student lab learning workbook focused on Cloud Computing using Google Cloud Services hands-on learning experiences.

Google Cloud does offer several ways for students to access its services for free or at a reduced cost, which can be utilized in such a workbook:
Google Cloud Free Trial: Students can sign up for a free Google Cloud account with a Gmail ID and avail free credits of $300, valid for 90 days. This trial includes various Google Cloud services but excludes certain resources like GPUs and Windows servers​​.
Google Cloud Free Tier: Google Cloud has over 20 services that are free to use up to a certain limit, with these usage limits being refreshed monthly. This includes resources like Compute Engine, Cloud Storage, and BigQuery, which are essential for cloud computing studies​​.
Google Cloud for Higher Education: This program is specifically for not-for-profit, accredited universities, providing credits to both students and faculty for teaching eligible courses. It's available only in selected countries​​.
Google Cloud Free for Students: Available to students who are at least 18 years old and enrolled in an accredited educational institution. Students need to provide proof of their student status and comply with certain billing and usage limitations. This program offers access to Google Cloud’s infrastructure and services, including Compute Engine, Storage and Databases, Artificial Intelligence and Machine Learning, and BigQuery​​.
With these resources, you can design a workbook that includes exercises such as deploying web applications, managing databases, and experimenting with machine learning models on the Google Cloud Platform. These practical activities can help students understand the concepts of cloud computing and gain hands-on experience.
The lab exercises in the workbook could cover a range of topics, such as:
Setting up and managing virtual machines using Compute Engine.
Data storage and management using Cloud Storage and Cloud SQL.
Building and deploying simple machine learning models using Google AI Platform.
Performing data analytics and queries using BigQuery.
Developing and hosting web applications using Google App Engine or Kubernetes Engine.
It's important to note that while these resources are free, they do come with certain limitations and conditions. Students should be aware of these to avoid incurring charges. For more detailed information and guidelines on how to access and use these services, students should explore Google Cloud's official documentation and support resources.

Students can start with Google Cloud's quickstarts, tutorials, and interactive walkthroughs available at
. Google Cloud for Education provides students with access to free online labs, quests, and courses through the Google Cloud Skills Boost platform

For beginners, there are several GCP project ideas available at
. These projects can help students gain hands-on experience with various Google Cloud services. To prepare for the Google Cloud Professional Cloud Architect certification, students can follow the exam guide and learning path provided at
. Additionally, a comprehensive Google Cloud Platform tutorial is available on YouTube
Students and faculty can also apply for Google Cloud Skills Boost credits, which can be used on any lab in the Google Cloud catalog
. Furthermore, Google offers a Career Readiness Program for faculty to lead students to proficiency in cloud infrastructure and data analytics with training and certification
Remember to take advantage of the free trial offered by Google Cloud, which includes $300 in credits for 3 months
. This trial allows students to practice and experiment with various Google Cloud services.
Want to print your doc?
This is not the way.
Try clicking the ⋯ next to your doc name or using a keyboard shortcut (
) instead.