Infrastructure and Environments for AI Deployment
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Table of Contents
- 1. Course Modules:
- 2. Module 1: Introduction to AI Infrastructure
- 3. Module 2: Local Development Environments
- 4. Module 3: Cloud Infrastructure for AI
- 5. Module 4: Containers and Virtual Machines
- 6. Module 5: Deployment Topologies and Networking
- 7.
- 8. Module 6: Security and Access Management
- 9. Module 7: Monitoring and Resource Optimization
- 10.
- 11. Module 8: Capstone Lab – Design Your Deployment Stack
- 12. Tools & Technologies Used:
- 13. Target Audience:
- 14. Global Learning Benefits:
Course Modules:
Module 1: Introduction to AI Infrastructure
What is infrastructure in the AI lifecycle?
Development vs. production environments
Overview of compute, storage, and networking
Module 2: Local Development Environments
Setting up Python, Conda, and Jupyter environments
Using Docker locally for reproducibility
Managing virtual environments and dependencies
Module 3: Cloud Infrastructure for AI
Comparing AWS, Google Cloud, and Azure
Setting up GPU and CPU instances
Intro to managed AI services (SageMaker, Vertex AI, Azure ML)
Module 4: Containers and Virtual Machines
Docker vs. VMs: pros and cons
Building Docker images for AI apps
Hosting models inside containers
Module 5: Deployment Topologies and Networking
On-premise, cloud-native, and hybrid AI systems
Load balancing, auto-scaling, and latency optimization
Setting up API gateways and endpoints
Module 6: Security and Access Management
Securing data and model endpoints
Authentication, IAM roles, and secrets management
Compliance (HIPAA, GDPR) and encrypted environments
Module 7: Monitoring and Resource Optimization
Tracking usage, cost, and compute efficiency
Logging system metrics and model performance
Auto-shutdown, scaling, and environment cleanup
Module 8: Capstone Lab – Design Your Deployment Stack
Choose a use case and build your infrastructure strategy
Configure a complete AI environment (local or cloud)
Submit documentation and a deployable demo project
Tools & Technologies Used:
Docker, Kubernetes (optional overview)
AWS EC2, SageMaker, GCP Compute Engine, Azure VM
Python, Git, Terminal, Conda
MLflow, Airflow (conceptual for MLOps)
Target Audience:
AI/ML developers and engineers
Data scientists deploying real-world solutions
DevOps teams working with AI workflows
Technical managers planning infrastructure strategies
Global Learning Benefits:
Understand the infrastructure needed for scalable AI
Set up environments that support development and production
Optimize cost, performance, and reliability in AI systems
Get hands-on experience with both local and cloud deployment setups
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