Description

Introduction

Computer Vision Applications Using SageMaker is a practical course designed to help learners build, train, and deploy computer vision models using AWS SageMaker. The course explores a wide range of use cases—from image classification and object detection to facial recognition and medical imaging—leveraging SageMaker’s built-in algorithms, prebuilt deep learning containers, and integration with frameworks like TensorFlow and PyTorch. By the end, participants will be able to implement real-world computer vision workflows in the cloud with scale and efficiency.

Prerequisites

Participants should have:

• A foundational understanding of machine learning and deep learning.

• Experience with Python and relevant libraries such as NumPy, OpenCV, or PIL.

• Basic familiarity with AWS SageMaker and S3.

• Optional: Exposure to convolutional neural networks (CNNs).

Table of Contents

1. Introduction to Computer Vision and SageMaker
    1.1 What is Computer Vision?
    1.2 Key Use Cases and Industry Applications
    1.3 Overview of SageMaker for Vision Tasks

2. Preparing Image Data for Training
    2.1 Dataset Formats and Labeling Tools (Ground Truth)
    2.2 Image Preprocessing and Augmentation
    2.3 Organizing Datasets for SageMaker

3. Image Classification with SageMaker
    3.1 Using SageMaker’s Image Classification Algorithm
    3.2 Custom CNN Models with TensorFlow or PyTorch
    3.3 Training, Evaluation, and Metrics

4. Object Detection and Localization
    4.1 Built-in Object Detection Algorithms (SSD, YOLO)
    4.2 Annotating Data with Ground Truth Labeling Jobs
    4.3 Training and Deploying Object Detection Models

5. Advanced Computer Vision Techniques
    5.1 Transfer Learning with Pretrained Models (ResNet, EfficientNet)
    5.2 Image Segmentation and Masking
    5.3 Facial Recognition and Pose Estimation

6. Model Deployment and Inference
    6.1 Real-time Endpoints vs Batch Transform
    6.2 Optimizing Inference with SageMaker Neo
    6.3 Monitoring Model Performance and Drift

7. Automating Vision Workflows
    7.1 SageMaker Pipelines for End-to-End Vision Projects
    7.2 Continuous Retraining with Updated Image Data
    7.3 Integration with Lambda, Step Functions, and S3

8. Real-World Computer Vision Project
    8.1 Defect Detection in Manufacturing Images
    8.2 Dataset Preparation, Model Training, and Inference
    8.3 Business Metrics and ROI of the Solution

AWS SageMaker provides a robust and scalable environment to implement computer vision solutions efficiently. With native support for deep learning, automated data labeling, and flexible deployment options, SageMaker accelerates vision-based ML projects from experimentation to production. This course equips learners to tackle real-world challenges using state-of-the-art computer vision tools and cloud-native workflows.