Cloud Computing with AI ( Associate Lvl 30 )
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.
30 hr
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.
- Session 1: Introduction to Google Cloud and
- 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.
- Each session will involve a significant portion of
Our instructors are certified Google Cloud professionals with practical
experience in both cloud computing and AI. They are skilled at delivering engaging, hands-on training sessions.- Total Credit Hours: 30
- Session Length: 3 hours per session
- Number of Sessions: 10
- Delivery Method: Remotely