Cloud Computing with AI ( Associate Lvl 30 )

EGP7,000.00

This focused training program spans 30 credit hours, delivered across 10 sessions of 3 hours each. The program is designed to provide a foundational understanding of Google Cloud core services
alongside key skills in leveraging AI tools and services offered by Google Cloud. This is a "one-level"
program providing both cloud basics and an introduction to AI on the platform without targeting a
specific certification level.

Upon completion of this training program, participants will be able to:

Understand Core Google Cloud Concepts: Grasp the fundamentals of Google Cloud Platform (GCP) architecture and essential services.

Utilize Core GCP Services: Use essential GCP services,
such as Compute Engine, Cloud Storage, and Virtual
Private Cloud (VPC).

Explore AI Concepts and Google Cloud AI: Understand
fundamental AI and Machine Learning concepts relevant
to practical applications.

Use Vertex AI Platform: Train and deploy basic Machine
Learning models using Vertex AI.

Work with Select Google Cloud AI Services: Leverage
popular Google Cloud AI services such as Vision AI or
Natural Language AI.

Integrate Gemini API: Learn how to use the Gemini API for
specific use cases.

Apply AI to Simple Scenarios: Understand how to
approach basic problems with AI and machine learning
solutions.

Understand Basic Security Practices: Be aware of basic
security considerations in cloud and AI applications.
The program is structured into 10 sessions, each lasting 3 hours. this outline will cover core topics, with a focus on hands-on practice.

Session 1: Introduction to Google Cloud and
Cloud Concepts

Overview of GCP Services, Regions, Zones, and Billing.

Core Cloud Computing concepts and models.

Hands-on: Navigating the Google Cloud Console and setting up an account.

2. Session 2: Compute and Networking Basics Working with Compute Engine:

Virtual Machines and instances.

Networking essentials: VPC, subnets, firewalls, and routes.

Hands-on: Creating a VM Instance and a basic VPC.

3. Session 3: Storage and Data Services Overview of Cloud Storage:

buckets, objects, and storage tiers.

Connecting and managing Cloud SQL databases.

Hands-on: Creating storage buckets and connecting to a Cloud SQL Instance.

4. Session 4: Introduction to AI and Machine Learning Core AI and Machine Learning concepts.

Types of Machine Learning: supervised, unsupervised and reinforcement learning.

Hands-on: Basic concepts of data cleaning and preparation for ML models.

5. Session 5: Overview of Vertex AI

Overview of the Vertex AI platform and core functionalities.

Creating and Training simple Machine Learning models in Vertex AI.

Hands-on: Creating a simple image classification model.

6. Session 6: Selected Google Cloud AI Services

Introduction to one key AI Service (e.g., Vision AI or Natural Language API).

Use cases and hands-on implementation.

Hands-on: Using chosen AI service for a selected use case.

7. Session 7: Introduction to Gemini API

Overview of Gemini API capabilities and use cases.

Basic Gemini API integration concepts and examples.

Hands-on: Initial implementation of basic Gemini API calls.

8. Session 8: Integrating Gemini API into a Simple Application

Hands-on Implementation of the API into an application, and basic prompts.

Hands-on: Implement prompts with practical use cases.

9. Session 9: Security and Best Practices Security basics:

IAM, access control, and data protection.

Best practices for AI application security.

Hands-on: Basic IAM configuration and security principles.

10. Session 10: Review, Troubleshooting and Next Steps

Project review, Q&A, and troubleshooting common issues.

Final session and overview of how to continue learning.
Each session will involve a significant portion of
hands-on work within Google Cloud, Vertex AI and
the use of provided AI APIs.

Guided Labs: Step-by-step labs focusing on specific GCP and AI services.

Practical Scenarios: Implementation of AI solutions for practical scenarios.

Individual Project: Focusedexercises to solidify understanding.
Total Credit Hours: 30

Session Length: 3 hours per session

Number of Sessions: 10

Delivery Method: Remotely

Cloud Computing with AI ( Associate Lvl 30 )

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