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Large-Scale Image Classification Using Distributed ML on AWS EMR and SageMaker

Large-Scale Image Classification

Large-Scale Image Classification Using Distributed ML on AWS

  • Use Case: Classifying massive image datasets in domains such as medical imaging, satellite imagery, e-commerce product images, and autonomous vehicles. The system must handle high-volume, high-dimensional data efficiently and with scalable distributed processing.

Objective

  • Build a distributed ML pipeline for large-scale image classification.

    Efficiently process and train models on large datasets using parallel and distributed computation.

    Leverage AWS services to scale training, optimize resource usage, and reduce overall training time.

    Deploy trained models for inference in the cloud or at the edge.

Project Description

  • This project focuses on processing and classifying large-scale image datasets using a combination of AWS EMR and AWS SageMaker:

Data Preprocessing on EMR

  • Use Spark or Hadoop on EMR to clean, normalize, and transform images into a format suitable for ML.
  • Parallelize image feature extraction across the cluster for efficiency.

Model Training on SageMaker

  • Train deep learning models (e.g., CNNs, ResNet, EfficientNet) using distributed GPU instances.

    Use SageMaker’s distributed training capabilities for large datasets.

    Optionally implement hyperparameter tuning using SageMaker Automatic Model Tuning.

Deployment & Inference

  • Deploy trained models on SageMaker endpoints or AWS Lambda for serverless inference.
  • Monitor model performance and retrain periodically with updated datasets.
    • Azure Services Used :
    Category AWS Service / Technology Purpose
    Big Data Processing AWS EMR Distributed preprocessing of large image datasets using Spark/Hadoop.
    Machine Learning Amazon SageMaker Train deep learning models using distributed GPU/CPU resources.
    Data Storage Amazon S3 Store raw images, processed data, and trained model artifacts.
    Monitoring Amazon CloudWatch Track EMR cluster, SageMaker jobs, and model performance metrics.
    Compute Scaling SageMaker Managed Spot / EMR Auto Scaling Dynamically scale resources for cost efficiency.
    Security & Access AWS IAM Secure access to S3, EMR, and SageMaker resources.
    Workflow Orchestration AWS Step Functions (Optional) Orchestrate preprocessing, training, and deployment tasks.
    Notification Amazon SNS Notify stakeholders when training or inference completes.