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AI+ Cloud™ 2.0
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Module 1: Fundamentals of Artificial Intelligence (AI) and Cloud
1.1 Module Introduction: Fundamentals of AI and Cloud
1.2 Introduction: Introduction to AI and its Application
1.3 Basic AI Concepts
1.4 Applications of AI
1.5 Introduction: Overview of Cloud Computing and Its Benefits
1.6 Understanding Cloud Computing
1.7 Key Benefits of Cloud Computing
1.8 Introduction: Benefits and Challenges of AI-Cloud Integration
1.9 Advantages of AI-Cloud Integration
1.10 Addressing Challenges in AI Cloud Integration
1.11 Module Summary: Fundamentals of AI and Cloud
Module 2: Introduction to Artificial Intelligence
2.1 Module Introduction: Introduction to Artificial Intelligence
2.2 Introduction: Basic Concepts and Principles of AI
2.3 Understanding the Foundations
2.4 Key Components of AI
2.5 Introduction: Machine Learning and its Applications
2.6 Introduction to Machine Learning
2.7 Types of Machine Learning
2.8 Practical Applications of Machine Learning
2.9 Introduction: Overview of Common AI Algorithms
2.10 Essential AI Algorithms
2.12 Introduction: Introduction to Python Programming for AI
2.13 Python Basics for AI
2.14 Python Libraries for AI
2.15 Module Summary: Introduction to Artificial Intelligence
Module 3: Fundamentals of Cloud Computing
3.1 Module Introduction: Fundamentals of Cloud Computing
3.2 Introduction: Cloud Service Models
3.3 Introduction to Cloud Service Models and IaaS
3.4 Platform-as-a-Service
3.5 Software as a Service
3.6 Introduction: Cloud Deployment Models
3.7 Types of Cloud Deployment Models
3.8 Public Cloud
3.9 Private Cloud
3.10 Hybrid Cloud
3.11 Hands-on Activity
3.12 Introduction: Key Cloud Providers and Offerings (AWS, Azure, Google Cloud, IBM Cloud)
3.13 AWS: Amazon's Cloud Ecosystem What
3.14 Azure: Microsoft's Cloud Solutions
3.15 Google Cloud: Innovation and Scalability
3.16 Module Summary: Fundamentals of Cloud Computing
Module 4: AI Services in the Cloud
4.1 Module Introduction: AI Services in the Cloud
4.2 Introduction: Integration of AI Services in Cloud Platforms
4.3 Overview of Cloud AI Services
4.4 Integrating Cloud AI Services
4.5 Introduction: Working with Pre-built Machine Learning Models
4.6 Leveraging Pre-built Models
4.7 Practical Application
4.8 Analyzing Results and Fine-tuning
4.9 Introduction: Working Principal of Cloud Base AI Tools
4.10 Overview of Cloud-based AI Development Tools
4.11 Module Summary: AI Services in the Cloud
Module 5: AI Model Development in the Cloud
5.1 Module Introduction: AI Model Development in the Cloud
5.2 Introduction: Building and Training Machine Learning Models
5.3 Traditional Machine Learning Model Development
5.5 Building Machine Learning Models with AutoML
5.7 Introduction: Model Optimization and Evaluation
5.8 Hyperparameter Tuning: Delve into hyperparameter tuning techniques for optimizing model performance
5.9 Evaluation Metrics and Techniques
5.10 Interpretability and Explainability
5.11 Introduction: Collaborative AI Development in a Cloud Environment
5.12 Version Control for Machine Learning Projects
5.13 Collaborative Development Platforms
5.14 Model Deployment and Sharing
5.15 Module Summary: AI Model Development in the Cloud
Module 6: Cloud Infrastructure for AI
6.1 Module Introduction: Cloud Infrastructure for AI
6.2 Introduction: Setting Up and Configuring Cloud Resources
6.3 Infrastructure as Code (IaC)
6.4 Introduction: Scalability and Performance Considerations
6.5 GPU and TPU Utilization
6.6 Auto-Scaling Strategies
6.7 Introduction: Data Storage and Management in the Cloud
6.8 Data Security and Compliance
6.9 Data Lifecycle Management
6.10 Module Summary: Cloud Infrastructure for AI
Module 7: Deployment and Integration
7.1 Module Introduction: Deployment and Integration
7.2 Introduction: Strategies for Deploying AI Models in the Cloud
7.3 Popular Deployment Strategies & Pattern
7.4 Platform-Specific Deployment
7.5 Introduction: Integration of AI Solutions with Existing Cloud-based Applications
7.6 Cloud Application Architecture
7.7 Microservices and AI
7.8 Data Integration Considerations
7.9 Introduction: API Usage and Considerations
7.10 API Design for AI Services
7.11 Testing Apis: Learn Testing Apis Through Various Tools Like Postman or Other Tool
7.12 Module Summary: Deployment and Integration
Module 8: Future Trends in AI + Cloud Integration
8.1 Module Introduction: Future Trends in AI+ Cloud Integration
8.2 Introduction: Introduction to Future Trends
8.3 Introduction to Explainable AI or XAI
8.4 Federated Learning
8.5 AI for Good
8.6 Quantum Computing and AI
8.7 Introduction: AI Trends Impacting Cloud Integration
8.8 Edge AI and Hybrid Cloud
8.9 Serverless AI
8.10 AutoML and Automated MLOps
8.11 Responsible AI in the Cloud
8.12 Module Summary: Future Trends in AI+ Cloud Integration
Module 9: Hands on Examples
9.1 Module Introduction: Hands on Examples
9.2 Introduction: Applying AI and Cloud Concepts to Solve a Real-world Problem
9.3 Applying AI and Cloud Concepts to Solve a Real-World Problem
9.4 Module Summary: Hands on Examples
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AI+ Cloud™ 2.0