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Deploy and monitor a machine learning workflow for Image Classification

An image-classification project for Udacity's AWS Machine Learning Fundamentals Nanodegree.

Table of Contents

  1. Overview
  2. File Contents
  3. Step Function
  4. Inferences

Overview

This is a project in Udacity’s AWS Machine Learning Fundamentals Nanodegree geared towards building an ML workflow.

The project uses a sample dataset called CIFAR to simulate an image classification model. The machine learning workflow for Image Classification is deployed using AWS resources such as, sagemaker and boto3 SDKs, image-classification built-in algorithm, Lambda functions and Step Function Workflow, and SageMaker model monitor and endpoint.

The CIFAR dataset is open source and can be downloaded at: https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz

The goal of this project is to:

  1. Build an image classification model that differentiates between bicycles and motorcycles.
  2. Deploy the model for inference using AWS Lambda functions and AWS Step functions.

File Contents

  • starter.ipynb : Jupyter notebook with solution and visualisations

  • lambda.py : All three Amazon Lambda functions later invoked in Step Functions

  • StepFunction.json : Exported JSON for Step Function flowchart

  • stepfunction/ : Image directory

Step Function

step_function

Inferences

The best image classification model has a test accuracy of 0.848958.

The visual below shows whether the images are inferred as a motorbike or a bicycle with their probability scores. Text is red if it is below the confidence threshold of 0.93.

viz

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