AI+ Cloud™

Cloud-AI Fusion: Learn to integrate AI into scalable cloud environments

Advanced Infrastructure: Master CI/CD, cloud AI models, and deployment strategies

Capstone Project: Gain hands-on experience with real-world applications

Future-Ready Skills: Prepares professionals to lead AI-powered cloud innovation

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Includes:

Ideal candidates for this course:

Cloud AI Integration Specialist. Focuses on integrating AI tools into cloud systems, optimizing cloud performance, scalability, and security.
AI Cloud Architect. Designs AI-powered cloud infrastructure, creating scalable, efficient, and secure cloud environments for organizations.
Cloud Automation Expert. Implements AI-driven automation tools for managing cloud infrastructure, reducing manual intervention and improving operational efficiency.
AI Cloud Data Scientist. Uses AI algorithms and data analytics to analyze cloud-based data, providing insights for better decision-making and resource management.
Cloud Security AI Specialist. AI technologies are applied to enhance cloud security, detecting anomalies, predicting threats, and ensuring robust protection of cloud.

Prerequisites

A foundational understanding of key concepts in both artificial intelligence and cloud computing.

Fundamental understanding of computer science concepts like programming, data structures, and algorithms.

Familiarity with cloud computing platforms like AWS, Azure, or GCP.

Basic knowledge of mathematics as it important for machine learning, which is a core component of AI+ Cloud™ program.

Content

Course Overview.

Course Introduction Preview

Module 1: Fundamentals of Artificial Intelligence (AI) and Cloud .

1.1 Introduction to AI and Its Application
1.2 Overview of Cloud Computing and Its Benefits
1.3 Benefits and Challenges of AI-Cloud Integration

Module 2: Introduction to Artificial Intelligence.

2.1 Basic Concepts and Principles of AI
2.2 Machine Learning and Its Applications
2.3 Overview of Common AI Algorithms
2.4 Introduction to Python Programming for AI

Module 3: Fundamentals of Cloud Computing.

3.1 Cloud Service Models
3.2 Cloud Deployment Models
3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud)

Module 4: AI Services in the Cloud.

4.1 Integration of AI Services in Cloud Platform
4.2 Working with Pre-built Machine Learning Models
4.3 Introduction to Cloud-based AI tools

Module 5: AI Model Development in the Cloud .

5.1 Building and Training Machine Learning Models
5.2 Model Optimization and Evaluation
5.3 Collaborative AI Development in a Cloud Environment

Module 6: Cloud Infrastructure for AI.

6.1 Setting Up and Configuring Cloud Resources
6.2 Scalability and Performance Considerations
6.3 Data Storage and Management in the Cloud

Module 7: Deployment and Integration.

7.1 Strategies for Deploying AI Models in the Cloud
7.2 Integration of AI Solutions with Existing Cloud-Based Applications
7.3 API Usage and Considerations

Module 8: Future Trends in AI+ Cloud Integration .

8.1 Introduction to Future Trends
8.2 AI Trends Impacting Cloud Integration

Module 9: Capstone Project .

9.1 Applying AI and Cloud Concepts to Solve a Real-world Problem

Optional Module: AI Agents for Cloud Computing.

1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents

After completing this course you will be able to:

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