Build & Deploy Serverless Apps on AWS: Design and Secure
About this course
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.
68/100
CourseAsk score
- What the provider tells you
- 32/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- analyze images and detect objects
- extract text from images
- evaluate content for safety
- automate workflows with AWS Lambda
- design serverless pipelines using AWS Rekognition and SNS
Price shown by Coursera — confirm on their site.
Enroll on CourseraYou'll be redirected to Coursera to complete enrollment.
- Listed & compared by CourseAsk
- English · 0
Udemy
Coursera
Compared on these lists
Where this course ranks against the alternatives.