AWS Serverless & AI/ML Project-Based Cloud Specialization

By the end of this course, learners will be able to analyze XML and JSON structures, build and execute an XML-to-JSON converter, deploy serverless code using AWS Lambda, and create and test an API Gateway endpoint for real-world integration. This hands-on course guides learners through the complete lifecycle of designing, coding, deploying, and validating a fully functional XML-to-JSON conversion workflow on AWS. Learners benefit by gaining practical experience with serverless development, understanding cloud-based data processing, and applying AWS services to solve real integration challenges. The course strengthens both foundational knowledge and applied cloud skills, making it highly valuable for developers, cloud beginners, and professionals working with data transformation pipelines. What makes this course unique is its end-to-end case-study approach, where learners not only write the conversion logic but also deploy it as a serverless, scalable API. Instead of theoretical lessons, each module walks step-by-step through real code, real deployment, and real AWS interactions—ensuring learners build confidence through practical execution. This project-driven design prepares learners to apply these skills immediately in workplace or production environments.

Learners will analyze images, detect objects and faces, extract text, evaluate unsafe content, and automate workflows using AWS Rekognition and Lambda. This hands-on course equips participants with the essential skills to build intelligent, cloud-native image and video analysis solutions. Throughout this project-driven journey, learners gain practical experience with object and label detection, image moderation, facial landmarks, celebrity recognition, face comparison, and text extraction. They also learn how to design serverless pipelines that integrate Rekognition with AWS Lambda and SNS to trigger automated processing. By completing the course, learners will be able to confidently apply deep-learning powered visual intelligence features within real-world applications—without managing complex infrastructure. What makes this course unique is its end-to-end, scenario-based approach that mirrors real industry use cases. Instead of abstract theory, learners work directly with live AWS services, exploring how Rekognition functions inside production-ready serverless environments. Whether for content moderation, security automation, metadata extraction, or intelligent media workflows, this course provides the practical foundation needed to implement AI-driven solutions at scale.

Learners will build, test, and optimize AWS Lambda functions, integrate them with API Gateway, and apply serverless logic to real-world arithmetic operations. This hands-on course empowers learners to design API request structures, validate GET and POST workflows, and enhance Lambda logic for secure and efficient execution. Throughout the course, learners gain practical experience implementing Python-based Lambda functions, configuring API Gateway endpoints, and interpreting CloudWatch logs to resolve errors. By completing guided demonstrations and real API tests, learners develop the confidence to deploy event-driven microservices without managing servers. What makes this course unique is its end-to-end case study approach, walking learners through every step of building a functional serverless application—from foundational concepts to advanced method-selection logic. Instead of abstract theory, each lesson builds directly toward a working, testable system. By the end, learners will be fully prepared to create, troubleshoot, and scale their own serverless workflows using AWS Lambda, making this course ideal for beginners and professionals seeking practical cloud skills.