Meta's SAM and Vision Transformers with AWS Rekognition, explained using intuitive math and real pipelines

What you'll learn
- Build an end-to-end auto-labeling pipeline using Segment Anything (SAM) for large-scale image datasets
- Understand how Vision Transformers (ViTs) work internally, including patch embeddings and self-attention
- Explain the core mathematics behind SAM, including mask decoding and prompt conditioning
- Run GPU-accelerated segmentation workloads efficiently using modern deep-learning stacks
- Compare SAM ViT-B, ViT-L, and ViT-H models and choose the right one for cost, speed, and accuracy
- Integrate AWS Rekognition for high-level object detection and metadata extraction
- Combine AWS Rekognition outputs with SAM masks to create precise, pixel-level labels
- Visualize segmentation masks, bounding boxes, and confidence scores for model debugging
- Analyze trade-offs between open-source CV models and managed cloud services
- Image Segmentation
- How to Use Open Source Models in AWS Sagemaker
- Optimize performance and memory usage when running SAM on large images
- Use AWS-based pipelines to scale computer-vision workloads reliably
- Bridge the gap between theory (math + models) and practical production pipelines
- AWS Rekognition
- Object Detection

Requirements
- Basic Python
- HS math

Description
Building a successful computer vision product starts with two things:strong foundationsandreal, scalable systems.

In this course, you'll learn how tobuild your own computer vision startup–style pipelineusingMeta's Segment Anything Model (SAM),Vision Transformers (ViTs), andAWS Rekognition—while actually understanding themath and intuition behind how these models work.

We begin by exploringVision Transformers from the ground up, focusing on clear, intuitive explanations of patch embeddings, attention mechanisms, and model representations. From there, we dive intoMeta's SAM architecture, explaining how prompts, embeddings, and mask decoding work together to produce high-quality segmentation results—without treating the model as a black box.

You'll then see how these open-source models fit intoreal-world systems. We integrateAWS Rekognitionfor high-level detection and metadata extraction, and combine it with SAM to createautomated, pixel-level labeling pipelines—the kind used by modern ML teams to scale dataset creation.

A strong emphasis is placed onvisualization and practical understanding. You'll inspect masks, bounding boxes, confidence signals, and failure cases, and learn how mathematical concepts translate directly into model behavior you can observe and debug.

By the end of the course, you won't just know how to run SAM or call an AWS API. You'll understandwhy the models work, how tocombine managed cloud services with open-source research, and how to think like someone building areal computer vision startup, not just a demo.

This course is ideal if you want to go beyond surface-level tutorials and gain aclear, intuitive understanding of modern computer vision systems—from math to production pipelines.

Who this course is for:
- Machine Learning Engineers who want to build real-world computer vision pipelines beyond toy examples
- Computer Vision Engineers looking to apply SAM and Vision Transformers in production workflows
- Data Scientists who want to automate image labeling and accelerate dataset creation
- AI Engineers interested in combining open-source vision models with AWS services
- Software Engineers transitioning into applied machine learning and computer vision
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