AI+ Cloud™

Executive Summary

The AI+ Cloud™ program equips developers and IT professionals to integrate AI with cloud computing efficiently.

• Master AI concepts, machine learning, and cloud infrastructure.

• Gain hands-on experience with AWS, Azure, and Google Cloud services.

• Learn to deploy AI models, optimize performance, and integrate with cloud-based applications.

• Complete a capstone project to apply AI+Cloud skills in real-world scenarios.

Learning Outcomes

Upon completion, participants will be able to:

• Understand AI fundamentals and cloud computing principles.

• Develop machine learning models using Python and popular libraries (NumPy, Pandas, Scikit-learn).

• Utilize cloud platforms (AWS, Azure, GCP) to deploy AI solutions.

• Optimize AI models and evaluate performance with relevant metrics.

• Integrate AI services into cloud-based applications using APIs and microservices.

• Apply best practices for scalability, security, and ethical AI in the cloud.

• Explore emerging trends: Edge AI, AutoML, Federated Learning, Serverless AI, and Quantum Computing.

Course Modules

Module 1 – AI & Cloud Fundamentals

• Introduction to AI: concepts, applications, and methodologies

• Overview of Cloud Computing: service models (IaaS, PaaS, SaaS), deployment models (public, private, hybrid)

• Benefits & challenges of AI-Cloud integration

Module 2 – Introduction to Artificial Intelligence

• Core AI concepts and components (ML, NLP, Computer Vision)

• Machine Learning fundamentals: Supervised, Unsupervised, Reinforcement Learning

• Python programming for AI: basics and libraries

• Hands-on: Implement basic AI tasks

Module 3 – Cloud Computing Fundamentals

• Cloud service and deployment models

• Major cloud providers: AWS, Azure, Google Cloud

• Hands-on: Create virtual machines, deploy web services

Module 4 – AI Services in the Cloud

• Cloud-based AI services (AWS AI, Azure Cognitive Services, GCP AI)

• Pre-built ML models and tools for AI development

• Hands-on: Integrate AI services into cloud applications

Module 5 – AI Model Development in the Cloud

• Build, train, and optimize ML models

• Hyperparameter tuning and evaluation metrics

• AutoML for automated model development

• Collaborative development using GitHub/GitLab

• Hands-on: Build and train ML models

Module 6 – Cloud Infrastructure for AI

• Infrastructure as Code (IaC) using Terraform

• Scalability and performance: GPU/TPU utilization, auto-scaling

• Data storage, management, security, and compliance

Module 7 – Deployment and Integration

• Deploy AI models with popular patterns (blue-green, canary)

• Integration with cloud applications and microservices

• API design and testing for AI services

Module 8 – Future Trends in AI+Cloud

• Explainable AI (XAI), Federated Learning, AI for Good

• Edge AI, Serverless AI, AutoML, Responsible AI

• Quantum computing and AI opportunities

Module 9 – Hands-on Real-world Projects

• Diabetes prediction using ML

• Build & deploy image classification apps with GCP AutoML, TensorFlow.js, and Flask

• Deploy custom ML models on GCP using REST API, model version monitoring