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

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.

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

Students can start with Google Cloud's quickstarts, tutorials, and interactive walkthroughs available at

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. Google Cloud for Education provides students with access to free online labs, quests, and courses through the Google Cloud Skills Boost platform

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For beginners, there are several GCP project ideas available at

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. 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

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. Additionally, a comprehensive Google Cloud Platform tutorial is available on YouTube

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Students and faculty can also apply for Google Cloud Skills Boost credits, which can be used on any lab in the Google Cloud catalog

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. Furthermore, Google offers a Career Readiness Program for faculty to lead students to proficiency in cloud infrastructure and data analytics with training and certification

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Remember to take advantage of the free trial offered by Google Cloud, which includes $300 in credits for 3 months

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. This trial allows students to practice and experiment with various Google Cloud services.