Cloud Computing with AI ( Expert Lvl 60 )

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

1. Understand Core Google Cloud Concepts: Grasp the fundamentals of Google Cloud Platform (GCP) architecture, services, and pricing models.

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

3. Explore AI Concepts and Google Cloud AI: Understand fundamental AI and Machine Learning concepts and how they apply to real-world problems.

4. Use Vertex AI Platform: Create and manage Machine Learning models using Google's Vertex AI platform.

5. Work with Google Cloud AI Services: Leverage popular Google Cloud AI services such as Vision AI, Natural Language AI, and Translation AI.

6. Integrate Gemini API: Learn to integrate the Gemini API into web and mobile applications.

7. Apply AI to Practical Scenarios: Understand how to apply AI and machine learning to solve real-world business problems and workflows.

8. Implement Basic Cloud and AI Security Practices: Understand basic security considerations within a cloud environment and AI applications.

60 hr

The program is structured into 20 sessions, each lasting 3 hours. The curriculum is designed to seamlessly integrate cloud fundamentals with AI concepts and applications.

1. Part 1: Google Cloud Fundamentals and Core
   Services 

• Session 1: Introduction to Google Cloud Platform(GCP): Overview of GCP services, regions, and zones, and foundational cloud computing concepts.
• Session 2: Core GCP Services and IAM:
  Projects, Identity and Access Management (IAM), billing, and resource management.
• Session 3: Compute Engine and Virtual Machines:
  Working with virtual machines, instance types, and lifecycle management.
• Session 4: Networking Basics and VPC:
  Virtual Private Cloud (VPC) networks, subnets, firewalls, and network configurations.
• Session 5: Cloud Storage Services:
  Overview of Cloud Storage options, including buckets, objects, and lifecycle policies.
• Session 6: Managed Databases with Cloud SQL:
  Deploying, managing, and connecting to Cloud SQL databases.
• Session 7: Introduction to Serverless with Cloud
  Functions:
  Concepts and deployments with Cloud
  Functions.

 

2.Part 2: Introduction to AI and Machine Learning Concepts

 

• Session 8: Introduction to AI and Machine Learning:
• Core AI concepts, Machine Learning basics, and types of Machine Learning.
• Session 9: Data Preparation for AI and ML:
  Data cleaning, preprocessing, feature engineering, and preparation for ML models.
• Session 10: Introduction to Vertex AI Platform:
  Overview of the Vertex AI platform, components, and core functionalities.
• Session 11: Creating and Training Machine
  Learning Models:
  Building and training Machine Learning models in Vertex AI, and model evaluation.

Part 3: Google Cloud AI Services and Integration

• Session 12: Vision AI with Google Cloud: Image recognition, object detection, and label detection using Vision API.
• Session 13: Natural Language AI with Google Cloud: Sentiment analysis, entity recognition, and language understanding with Natural Language API.
• Session 14: Translation AI and other AI APIs: Translation API, speech-to-text, and text-to-speech functionality.
• Session 15: Overview of the Gemini API: Introduction to the Gemini API capabilities and use cases.
• Session 16: Integrating Gemini API into Applications (Part1): Hands-on implementation with the API and basic prompts.
• Session 17: Integrating Gemini API into Applications (Part 2): Hands-on with more complex requests and use cases.

 

Part 4: Project, Troubleshooting and Next Steps

• Session 18: Security in Cloud and AI: Best practices for securing cloud resources and AI applications.
• Session 19: Hands-on Project Implementation: Designing and implementing a project integrating Cloud Services and AI.
• Session 20: Project Review, Troubleshooting, and Next Steps: Project presentation, troubleshooting, Q&A, and next steps for deeper learning.

• Each session will involve a significant portion of
  hands-on work, using the Google Cloud Console,
  and specific AI APIs and tools. This will include:

1. Guided Labs: Step-by-step labs focusing on specific Google Cloud and AI services.
2. Practical Scenarios: Real-world scenarios that integrate both cloud and AI aspects.
3. Individual Project: A Capstone Project that allows integration of multiple services.

 

Our instructors are certified Google Cloud professionals with extensive
experience in both cloud computing and artificial intelligence. They are
skilled at delivering engaging and informative training sessions.

• Total Credit Hours: 60
• Session Length: 3 hours per session
• Number of Sessions: 20
• Delivery Method: Remotely