DevOps and AI on AWS Specialization course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
A comprehensive specialization designed to equip learners with foundational skills in DevOps and AI on AWS. This program integrates automation, machine learning, and cloud infrastructure to build and deploy production-grade AI applications. With approximately 14–18 weeks of content, learners engage with hands-on labs, real-world scenarios, and guided projects across core domains: DevOps practices, AI/ML workflows, CI/CD pipelines, and operational excellence in AWS environments.
Module 1: Foundations of DevOps on AWS
Estimated time: 15 hours
- Introduction to DevOps principles: collaboration, automation, continuous integration, and delivery
- Core AWS services: EC2, S3, IAM, and CloudWatch
- Infrastructure as code (IaC) concepts using AWS CloudFormation and AWS CDK
- Monitoring and logging in AWS with CloudWatch and AWS Config
Module 2: AI and Machine Learning on AWS
Estimated time: 20 hours
- Overview of AI and ML capabilities on AWS
- Introduction to Amazon SageMaker and managed ML workflows
- Data preparation and preprocessing using AWS services
- Model training, evaluation, and deployment pipelines
Module 3: CI/CD for AI and ML Workloads
Estimated time: 16 hours
- Building CI/CD pipelines for machine learning applications
- Automating model testing, validation, and versioning
- Implementing MLOps practices using AWS CodePipeline and AWS CodeBuild
- Model deployment strategies: A/B testing, canary, and blue/green
Module 4: Monitoring, Security, and Optimization
Estimated time: 16 hours
- Monitoring AI applications with Amazon CloudWatch and AWS X-Ray
- Securing ML systems: IAM roles, data encryption, and VPCs
- Cost optimization and performance tuning for AI workloads
- Scaling AI applications using AWS Auto Scaling and Elastic Inference
Module 5: MLOps in Practice on AWS
Estimated time: 12 hours
- End-to-end MLOps workflow implementation
- Model drift detection and retraining automation
- Model explainability and auditability with Amazon SageMaker Clarify
Module 6: Final Project
Estimated time: 20 hours
- Design and deploy a CI/CD pipeline for an AI application on AWS
- Implement infrastructure as code and automated testing
- Monitor, secure, and optimize the deployed AI system
Prerequisites
- Familiarity with basic cloud computing concepts and AWS core services
- Basic understanding of machine learning and data science workflows
- Experience with programming (Python preferred) and command-line tools
What You'll Be Able to Do After
- Apply DevOps and MLOps principles to automate AI/ML workflows on AWS
- Design and deploy secure, scalable AI applications using AWS-native services
- Implement CI/CD pipelines tailored for machine learning projects
- Monitor, troubleshoot, and optimize production AI systems
- Advance toward roles such as Cloud DevOps Engineer, MLOps Engineer, or AI Infrastructure Engineer