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.
