COURSE CURRICULUM

Module Description:

Gain a strong foundational understanding of cloud computing concepts and the role AI plays in modern cloud infrastructures.

• Overview of Cloud Computing: History, Types (Public, Private, Hybrid), and Service Models (IaaS, PaaS, SaaS)
• Key Components of Cloud Computing Architecture
• Introduction to Artificial Intelligence and Machine Learning
• Intersection of Cloud Computing and AI: Benefits and Challenges
• Cloud Computing Market Leaders: AWS, Microsoft Azure, Google Cloud
• Introduction to Cloud Security and Compliance

Module Description:

Learn how to set up and configure cloud environments on the major cloud platforms: AWS, Azure, and Google Cloud.

• Creating and Configuring Virtual Machines and Cloud Storage
• Cloud Networking: Virtual Networks, Subnets, and Routing
• Hands-on with AWS Console, Azure Portal, and Google Cloud Console
• Understanding Cloud Pricing and Cost Management
• Using Cloud Identity & Access Management (IAM) to Control Permissions
• Best Practices for Cloud Security and Data Protection

Module Description:

Explore different data storage solutions in the cloud, along with methods for integrating AI technologies for data analytics and processing.

• Cloud Storage Services: Object Storage, Block Storage, File Storage
• Databases in the Cloud: Relational Databases (RDS), NoSQL Databases (MongoDB, DynamoDB)
• Data Warehousing: BigQuery, Redshift, and Synapse
• AI Integration for Big Data: ETL Processes, Data Pipelines
• AI-Based Data Analytics Tools and Frameworks
• Integrating AI Models with Cloud Databases for Real-Time Insights

Module Description:

Learn how to deploy and scale machine learning models on cloud platforms, integrating AI for automation and decision-making.

• Overview of AI/ML Services on AWS, Azure, and Google Cloud
• Building and Deploying Machine Learning Models Using Cloud AI Tools
• AutoML Features for Model Training and Evaluation
• Deploying Models Using Amazon SageMaker, Azure Machine Learning, and Google AI Platform
• Model Monitoring and Maintenance in the Cloud
• Ethical Considerations and Bias Mitigation in AI Models

Module Description:

Dive deep into specific cloud AI services like Chatbots, Computer Vision, and Natural Language Processing (NLP).

• Introduction to Cloud AI Services: Amazon Lex, Google Dialogflow, Microsoft Bot Framework
• Building Intelligent Chatbots for Customer Support and Business Automation
• Cloud-Based Computer Vision Services: Image Recognition, Object Detection
• Natural Language Processing (NLP) in the Cloud: Text Analysis, Sentiment Analysis
• Integrating AI APIs for Real-Time Applications
• Building AI-Powered Products: Virtual Assistants, Automated Image Tagging

Module Description:

Learn how to create highly scalable applications using serverless computing and automated cloud services.

• Introduction to Serverless Computing: AWS Lambda, Azure Functions, Google Cloud Functions
• Building Serverless Applications and Microservices
• Automating Cloud Infrastructure with Infrastructure as Code (IaC)
• Continuous Integration and Continuous Deployment (CI/CD) in the Cloud
• Scaling Applications Using Auto-Scaling and Load Balancers
• Optimizing Cloud Costs with Serverless Architecture

Module Description:

Understand how to secure cloud environments and ensure AI models are deployed and maintained securely and ethically.

• Cloud Security Fundamentals: Identity, Authentication, and Authorization
• Data Encryption and Key Management in Cloud Environments
• Compliance and Regulatory Requirements: GDPR, HIPAA, SOC2
• AI Security: Adversarial Attacks and Protecting AI Models
• Cloud Security Best Practices and Threat Detection
• Compliance Tools and Automation in Cloud Platforms

Module Description:

Learn to integrate cloud computing with IoT devices and use AI for real-time data processing.

• Introduction to Internet of Things (IoT) and Cloud Integration
• Building IoT Applications on Cloud Platforms
• AI for Real-Time IoT Data Processing and Analytics
• Connecting IoT Devices with AI for Predictive Maintenance
• Using Cloud and AI for Smart City Solutions
• Case Study: Cloud AI for Industrial IoT

Module Description:

Master how to leverage AI and cloud services to transform business data into actionable insights for decision-making.

• AI-Driven Business Intelligence Tools
• Integrating AI Models with BI Tools (Power BI, Tableau)
• Cloud-Based Analytics Platforms for Data Visualization
• AI for Predictive Analytics and Forecasting
• Building Custom Dashboards for Business Insights
• Real-Time Data Processing and Analytics with Cloud and AI

Module Description:

Understand the ethical considerations in AI and the future trends in cloud computing and AI integration.

• Ethical Issues in AI: Bias, Fairness, and Accountability
• AI Governance and Policy Making
• Future Trends in Cloud Computing: Serverless, Edge Computing
• AI Advancements: Deep Learning, Quantum Computing
• The Role of AI in Automating the Cloud Infrastructure
• Preparing for the Future: Job Roles, Skills, and Opportunities in AI & Cloud