AI+ Cloud™
About Course
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
Ready to get started?
To purchase this course, please add it to your cart and complete the checkout process.
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