Master Study AI

Infrastructure and Environments for AI Deployment

artificial-intelligence-ai.

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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