Module 1: Fundamentals of Artificial Intelligence (AI) and Cloud
- 1.1 Introduction to AI and its Application
- Basic AI Concept: Gain a solid understanding of the fundamental concepts, principles, and methodologies related to Artificial Intelligence.
- AI Applications: Explore real-world examples of AI, showcasing its impact on technology and business.
- 1.2 Overview of Cloud Computing and Its Benefits
- Understanding Cloud Computing: Uncover definition, Properties and Characteristics of Cloud Computing.
- Key benefits of Cloud Computing: Discover how businesses and projects use the cloud in real life, making things faster and more efficient
- 1.3 Benefits and Challenges of AI-Cloud Integration
- Advantages of AI-Cloud Integration: Explore the interactions between AI and cloud computing, focusing on enhanced scalability, accessibility, and collaborative development.
- Addressing Challenges in AI-Cloud Integration: Investigate challenges related to security, privacy and how to make smart integration decisions
Module 2: Introduction to Artificial Intelligence
- 2.1 Basic Concepts and Principles of AI
- Understanding the Foundations: Delve into the basic principles of artificial intelligence, exploring its core concepts and the underlying ideas that make AI possible
- Key Components of AI: Identify the key components that form the foundation of AI systems, including machine learning, natural language processing, and computer vision.
- 2.2 Machine Learning and Its Applications
- Introduction to Machine Learning: Explore the world of machine learning, understanding how computers can learn from data and improve their performance over time.
- Types of Machine Learning: Explore various types of machine learning like Supervised, Unsupervised, and Reinforcement Learning
- Practical Applications of Machine Learning: Discover real-world applications of machine learning, from recommendation systems and autonomous vehicles to healthcare diagnostics.
- 2.3 Overview of Common AI Algorithms
- Essential AI Algorithms: Introduce commonly used AI algorithms, such as regression, classification, clustering and Understand the strengths and limitations of each algorithm, exploring when to use them for various tasks.
- Hands On: Dive into practical application scenarios.
- 2.4 Introduction to Python Programming for AI
- Python Basics for AI: Learn the basics of Python programming, a versatile language widely used in AI development.
- Python Libraries for AI: Explore Python libraries essential for AI, including NumPy, Pandas, and Scikit-learn, to manipulate data and implement machine learning models.
Module 3: Fundamentals of Cloud Computing
- 3.1 Cloud Service Models
- Introduction to Cloud Services: Understand the basic concepts of cloud service models, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)
- IaaS: Building Blocks of Cloud Infrastructure: Explore Infrastructure as a Service, learning how it provides fundamental computing resources like virtual machines and storage
- PaaS: Platform for Application Development: Delve into Platform as a Service, exploring how it offers a platform for developers to build, deploy, and scale applications without managing underlying infrastructure.
- SaaS: Software Delivery via the Cloud: Understand Software as a Service and how it delivers software applications over the internet, eliminating the need for local installation and maintenance.
- 3.2 Cloud Deployment Models
- Public, Private, Hybrid: Deployment Choices: Explore different cloud deployment models, including public, private, and hybrid clouds, understanding the advantages and considerations for each.
- Public Cloud: Services for Everyone: Learn about public cloud deployments, where services are offered over the internet to a broad audience, with examples from major cloud providers
- Private Cloud: Tailored Solutions: Understand private cloud deployments designed for a specific organization, offering increased control and customization
- Hybrid Cloud: Combining the Best of Both Worlds: Explore hybrid cloud models, combining elements of both public and private clouds for flexibility and scalability. ·
- Hands-on Activity: 1. Create and deploy a virtual machine on AWS Deploying Web Services on Azure: 2. Set up a web application on Azure App Service
- 3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud)
- AWS: Amazon’s Cloud Ecosystem: Dive into Amazon Web Services (AWS), understanding its services, infrastructure, and its role as a leading cloud provider.
- Azure: Microsoft’s Cloud Solutions: Explore Microsoft Azure, its services, and how organizations leverage its cloud solutions for diverse applications.
- Google Cloud: Innovation and Scalability: Learn about Google Cloud Platform (GCP), its innovative services, and how it provides scalable solutions for businesses.
Module 4: AI Services in the Cloud
- 4.1 Integration of AI Services in Cloud Platforms
- Overview of Cloud AI Services: Explore cloud-based AI services offered by major providers (e.g., AWS AI services, Azure Cognitive Services, Google Cloud AI) and understand their capabilities.
- Integrating Cloud AI Services: Hands-on exercise on integrating AI services into cloud platforms to enhance applications
- 4.2 Working with Pre-built Machine Learning Models
- Leveraging Pre-built Models: Understand the concept of pre-built machine learning models available in cloud environments.
- Practical Application: Working with Pre-trained Models: Use a cloud-based service (e.g., Google Cloud Vision AI or Azure Computer Vision)
- Analyzing Results and Fine-tuning: Evaluate the results of using pre-built models and explore the possibilities of fine-tuning parameters for specific use cases.
- 4.3 Introduction to Cloud-based AI Tools
- Overview of Cloud-based AI Development Tools: Explore tools provided by cloud platforms for AI development, including notebooks, model training environments, and collaborative tools.
Module 5: AI Model Development in the Cloud
- 5.1 Building and Training Machine Learning Models
- Traditional Machine Learning Model Development: Explore traditional methods for developing machine learning models, covering foundational concepts, algorithms, and techniques in model development.
- Hands-on: Exercises on Building and Training Models using Code: Engage in practical exercises to build and train machine learning models through hands-on coding, implementing algorithms, and evaluating performance
- Building Machine Learning Models with AutoML: Discover the power of AutoML in simplifying the machine learning model development process, leveraging automated tools for efficient model creation.
- Hands-on exercises: Demonstration of Building a Machine Learning Model using AutoML: Dive into hands-on demonstrations illustrating the utilization of AutoML tools to build machine learning models swiftly.
- 5.2 Model Optimization and Evaluation
- Hyperparameter Tuning: Learn techniques to optimize model performance by tweaking parameters for better accuracy and efficiency in machine learning models.
- Evaluation Metrics and Techniques: Understand various metrics and techniques to assess model performance and choose the most suitable evaluation methods for different scenarios.
- Interpretability and Explainability: Gain insights into techniques for explaining and understanding machine learning models, making their decisions transparent and interpretable for stakeholders
- 5.3 Collaborative AI Development in a Cloud Environment
- Version Control for Machine Learning Projects: Master Git and other tools to track changes, collaborate effectively, and manage versions in machine learning projects for enhanced productivity and reproducibility
- Collaborative Development Platforms: Explore platforms like GitHub and GitLab to facilitate team collaboration, code sharing, and project management in machine learning development environments.
- Model Deployment and Sharing: Learn strategies and platforms for deploying machine learning models, enabling seamless integration into production environments and sharing insights with stakeholders.
Module 6: Cloud Infrastructure for AI
- 6.1 Setting up and Configuring Cloud Resources
- Infrastructure as Code (IaC): Learn to automate and manage infrastructure using tools like Terraform, ensuring consistent and scalable deployment for machine learning workflows.
- 6.2 Scalability and Performance Considerations
- GPU and TPU Utilization: Optimize machine learning workloads by harnessing the power of GPUs and TPUs for accelerated training and inference tasks.
- Auto-Scaling Strategies: Implement dynamic scaling strategies to adapt computing resources based on workload demands, ensuring efficient utilization and cost-effectiveness in machine learning deployments.
- 6.3 Data Storage and Management in the Cloud
- Data Security and Compliance: Explore strategies and technologies to safeguard sensitive data, ensuring compliance with regulations and protecting against breaches in machine learning environments.
- Data Lifecycle Management: Manage data from creation to disposal efficiently, ensuring quality, accessibility, and compliance throughout its lifecycle in machine learning workflows
Module 7: Deployment and Integration
- 7.1 Strategies for Deploying AI Models in the Cloud
- Popular Deployment Strategies & Pattern: Explore popular deployment patterns like blue-green, canary releases, and others for efficient and reliable deployment of machine learning models at scale.
- Platform-Specific Deployment: Learn to deploy machine learning models on various platforms like AWS, Azure, and Google Cloud, leveraging platform-specific features for optimal performance
- 7.2 Integration of AI Solutions with Existing Cloud-based Applications
- Cloud Application Architecture: Design scalable and resilient cloud-based architectures for machine learning applications, leveraging services like AWS, Azure, and Google Cloud for optimal performance.
- Microservices and AI: Explore the integration of microservices with AI, enabling modular and scalable architectures for building and deploying machine learning solutions.
- Data Integration Considerations: Address challenges and considerations in integrating diverse data sources, ensuring compatibility, quality, and reliability for effective machine learning workflows
- 7.3 API Usage and Considerations
- API Design for AI Services: Master designing APIs for AI services, covering protocols, authentication, and documentation to ensure interoperability, security, and ease of use.
- Testing APIs: Learn Testing Apis Through Various Tools Like Postman or Other Tools: Develop proficiency in testing APIs using tools like Postman, ensuring reliability, functionality, and performance in AI service deployments
Module 8: Future Trends in AI+ Cloud Integration
- 8.1 Introduction to Future Trends
- Introduction to Explainable AI or XAI: Explore methods to interpret and explain AI models, enhancing transparency and trustworthiness in decision-making processes for diverse stakeholders
- Federated Learning: Delve into decentralized machine learning techniques, enabling model training across distributed devices while preserving data privacy and security
- AI for Good: Harness AI’s potential to address global challenges, focusing on applications in healthcare, sustainability, education, and humanitarian efforts for societal benefits.
- Quantum Computing and AI: Explore the intersection of quantum computing and AI, unlocking possibilities for solving complex problems and optimizing machine learning algorithms with quantum processing power.
- 8.2 AI Trends Impacting Cloud Integration
- Edge AI and Hybrid Cloud: Implement AI models on edge devices and leverage hybrid cloud infrastructure, optimizing performance and privacy for decentralized applications.
- Serverless AI: Explore serverless computing for AI, enabling scalable and cost-efficient deployment without managing infrastructure, ideal for dynamic workloads.
- AutoML and Automated MLOps: Automate machine learning model selection, training, and deployment processes, streamlining ML operations and empowering developers with efficient AI solutions.
- Responsible AI in the Cloud: Integrate ethical considerations into cloud-based AI development, ensuring fairness, accountability, transparency, and privacy throughout the machine learning lifecycle.
Module 9: Hands-on Examples
- Exercise 1: Diabetes Prediction Using Machine Learning.
- Exercise 2: Building & Deploying an Image Classification Web App with GCP AutoML Vision Edge, Tensorflow.js & GCP App Engine
- Exercise 3: How to deploy your own ML model to GCP in 5 simple steps
- Exercise 4: Google Cloud Platform Custom Model Upload, REST API Inference and Model Version Monitoring.
- Exercise 5: Deploy Machine Learning Model in Google Cloud Platform Using Flask
GTR 
