Cloud Computing and AI - Syllabus

Instructor: Dr. Joshua Adams

Term: Fall 2025

Course Description

This course provides an in-depth exploration of cloud computing technologies and their application in Artificial Intelligence (AI). Students will gain hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) to deploy, manage, and scale AI applications. Topics include cloud infrastructure, machine learning services, serverless computing, and big data processing in the cloud.

Learning Objectives

  • Understand cloud computing architectures and service models (IaaS, PaaS, SaaS).
  • Utilize cloud platforms to deploy and manage AI/ML workloads.
  • Apply machine learning services (e.g., SageMaker, Azure ML, Vertex AI) to solve practical problems.
  • Design and implement serverless functions for AI applications.
  • Process and analyze large datasets in the cloud using big data technologies.
  • Evaluate the cost, performance, and scalability of different cloud-based AI solutions.
  • Explore ethical considerations and best practices in AI and cloud computing.

Assessment & Grading

  • Cloud-Based AI Labs – 30%
  • Midterm Exam – 25%
  • Final Cloud AI Project – 30%
  • Participation & Discussions – 15%

Course Projects

This course includes a significant project component focused on applying cloud computing to AI-related tasks. Students will work individually or in small groups to complete the following:

  • Cloud-Based Machine Learning Application: Design, deploy, and scale a machine learning application on a cloud platform (e.g., image recognition, sentiment analysis).
  • Serverless AI Pipeline: Implement a serverless pipeline for processing and analyzing data for an AI application.
  • Big Data Analysis for AI: Utilize cloud-based big data tools (e.g., Hadoop, Spark) to process and prepare data for a machine learning model.
  • Cost Optimization of Cloud AI Deployment: Analyze and optimize the cost of deploying and running an AI application in the cloud.