Build A Computer Vision Startup With SAM+Vision Transformers

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 foundations and real, scalable systems.In this course, you'll learn how to build your own computer vision startup-style pipeline using Meta's Segment Anything Model (SAM), Vision Transformers (ViTs), and AWS Rekognition-while actually understanding the math and intuition behind how these models work.We begin by exploring Vision Transformers from the ground up, focusing on clear, intuitive explanations of patch embeddings, attention mechanisms, and model representations. From there, we dive into Meta'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 into real-world systems. We integrate AWS Rekognition for high-level detection and metadata extraction, and combine it with SAM to create automated, pixel-level labeling pipelines-the kind used by modern ML teams to scale dataset creation.A strong emphasis is placed on visualization 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 understand why the models work, how to combine managed cloud services with open-source research, and how to think like someone building a real computer vision startup, not just a demo.This course is ideal if you want to go beyond surface-level tutorials and gain a clear, intuitive understanding of modern computer vision systems-from math to production pipelines.
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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