AWS Serverless and AI/ML Projects

Cloud computing is shifting toward serverless architectures, and integrating artificial intelligence is now essential for modern developers. This written guide helps you bridge the gap between cloud infrastructure and machine learning by walking you through real-world implementation patterns. You will transition from understanding basic cloud concepts to deploying fully functional, automated serverless workflows that leverage powerful pre-trained AI and ML services. Learn to: Understand foundational serverless concepts including AWS Lambda, API Gateway, and event-driven architectures; Configure managed AI services such as Amazon Rekognition, Polly, and Translate within serverless workflows; Build automated data pipelines that trigger machine learning inference upon file uploads; Implement modern infrastructure-as-code basics using AWS SAM to define cloud resources in text; Integrate generative AI capabilities using foundation models to build intelligent text-processing workflows; Apply security best practices using IAM roles and monitor workflows with cloud observability tools. The course starts with essential terminology, core serverless definitions, and basic security concepts. You will then progress through structured, step-by-step written projects that guide you through designing, deploying, and testing live cloud architectures. Designed for aspiring cloud engineers, developers, and tech enthusiasts who want hands-on cloud experience, this course requires no prior AWS or machine learning background. Start building your cloud portfolio with these practical serverless and AI projects today.