The aim of this project is to build ML Workflow on AWS Sagemaker
Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.
The image classification model can help the team in a variety of ways in their operating environment: detecting people and vehicles in video feeds from roadways, better support routing for their engagement on social media, detecting defects in their scones, and many more!
In this project, we'll be building an image classification model that can automatically detect which kind of vehicle delivery drivers have, in order to route them to the correct loading bay and orders. Assigning delivery professionals who have a bicycle to nearby orders and giving motorcyclists orders that are farther can help Unlimited optimize operations.
In this project, we'll use AWS Sagemaker to build an image classification model that can tell bicycles apart from motorcycles. we'll deploy our model, use AWS Lambda functions to build supporting services, and AWS Step Functions to compose the model and services into an event-driven application.
The dataset is CIFAR-100. The CIFAR dataset is open source and generously hosted by the University of Toronto at: https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz.