Cloud_curriculum.md

6-Week AI/ML Cloud-Based Course Curriculum

Week 1: Foundations of AI/ML in the Cloud

• Learning Objectives:
  • Understand core AI/ML concepts and the role of cloud platforms (AWS, Azure).
  • Learn Infrastructure as Code (Terraform) for managing cloud infrastructure.
• Topics:
  • Introduction to AI/ML: Overview of ML models, algorithms, workflows.
  • Cloud Platforms: AWS vs. Azure for AI/ML.
  • Terraform Basics: IaC, Terraform structure, and syntax.
  • Core ML Services: AWS Sagemaker, Azure ML Service.
• Hands-On Lab:
  • Create a Terraform script to set up an ML training environment in AWS or Azure.
• Homework:
  • Terraform lab for deploying cloud infrastructure.
  • Watch tutorials on AWS Sagemaker or Azure ML Service.

Week 2: Data Management and Processing for AI/ML in Cloud

• Learning Objectives:
  • Manage and process large datasets for ML in cloud environments.
  • Build data ingestion and transformation pipelines using cloud services.
• Topics:
  • Data Lakes & Warehouses: AWS S3, Azure Data Lake, Redshift, Synapse.
  • ETL Pipelines: AWS Glue, Azure Data Factory.
  • Terraform for Data Management: Automating data storage provisioning.
• Hands-On Lab:
  • Set up an ETL pipeline using AWS Glue or Azure Data Factory to preprocess data for ML.
• Homework:
  • Automate data storage provisioning with Terraform.
  • Write a report on data management strategies for AI in the cloud.

Week 3: Model Training and Tuning on Cloud Infrastructure

• Learning Objectives:
  • Train, tune, and manage ML models using cloud-based infrastructure.
  • Utilize cloud services for hyperparameter tuning and optimization.
• Topics:
  • Model Training: Training models using AWS Sagemaker or Azure ML Studio.
  • Hyperparameter Tuning: SageMaker Tuning Jobs, Azure HyperDrive.
  • Distributed Training: GPUs/TPUs in the cloud.
  • AutoML: AWS AutoPilot, Azure AutoML.
• Hands-On Lab:
  • Train an ML model on AWS Sagemaker or Azure ML Service, and optimize with hyperparameter tuning.
• Homework:
  • Experiment with AutoML to train a model and document the results.
  • Deploy a model using AWS Sagemaker or Azure ML endpoints.

Week 4: Deploying and Serving ML Models in the Cloud

• Learning Objectives:
  • Deploy and serve ML models using AWS Sagemaker, Azure ML, or Kubernetes.
  • Explore serverless architecture and microservices for scalable ML deployment.
• Topics:
  • Model Deployment: Deploying models on AWS Sagemaker, Azure ML, Kubernetes.
  • Containerization: Docker, Kubernetes, AWS Fargate for containerized ML.
  • Serverless for ML: AWS Lambda, Azure Functions for inference.
  • Terraform for Model Deployment: Automating deployment using Terraform.
• Hands-On Lab:
  • Deploy a model using AWS Sagemaker, Azure Kubernetes Service, or Lambda.
• Homework:
  • Research serverless architecture for AI/ML use cases.
  • Write a Terraform script to automate model deployment.

Week 5: MLOps – CI/CD Pipelines and Monitoring ML Models

• Learning Objectives:
  • Understand MLOps practices for automating AI/ML operations.
  • Build CI/CD pipelines and set up monitoring for deployed models.
• Topics:
  • MLOps Principles: CI/CD for ML, model versioning, experiment tracking.
  • CI/CD Pipelines: Automating pipelines with Jenkins, GitLab CI/CD, Azure DevOps.
  • Monitoring and Logging: AWS CloudWatch, Azure Monitor for model tracking.
  • Model Drift Detection: Automating retraining when performance degrades.
• Hands-On Lab:
  • Build a CI/CD pipeline for automating model training and deployment using AWS CodePipeline or Azure DevOps.
• Homework:
  • Set up monitoring and alerts for a deployed ML model.
  • Reflect on MLOps in enterprise AI applications.

Week 6: Capstone Project – Building an AI/ML Pipeline in the Cloud

• Learning Objectives:
  • Apply concepts learned to design, build, and deploy a full AI/ML pipeline.
  • Present a comprehensive AI solution from data ingestion to model deployment.
• Project Guidelines:
  • Step 1: Problem Definition: Identify a real-world problem for an AI/ML solution.
  • Step 2: Data Ingestion: Set up a cloud-based data pipeline for ingestion and preprocessing.
  • Step 3: Model Training: Use AWS Sagemaker or Azure ML Service to train and optimize a model.
  • Step 4: Model Deployment: Deploy the model using AWS Sagemaker, AKS, or Lambda.
  • Step 5: Monitoring: Implement monitoring to track model performance.
  • Step 6: Automate with Terraform: Automate provisioning of cloud resources.
• Final Presentation:
  • Present the end-to-end AI/ML pipeline, explaining decisions, challenges, and results.
  • Demonstrate the real-world impact and scalability of your project.

Capstone Project Examples

• Predictive Maintenance for Industrial Equipment: Ingest IoT sensor data, train a predictive model, and deploy it to an edge device.
• Customer Churn Prediction: Process customer data, build a churn prediction model, and deploy it for real-time inference.
• Sentiment Analysis for Social Media: Use social media text data, build an NLP model, and deploy it for real-time feedback.

Course Assessment

• Participation: Weekly labs and homework (40%).
• Capstone Project: Presentation and demonstration (60%).