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