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.
You are hired as a Machine Learning Engineer for a scone-delivery-focused logistics company, Scones Unlimited, and you’re working to ship an Image Classification model. 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, you'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 Scones Unlimited optimize their operations.
As an MLE, your goal is to ship a scalable and safe model. Once your model becomes available to other teams on-demand, it’s important that your model can scale to meet demand, and that safeguards are in place to monitor and control for drift or degraded performance.
In this project, you’ll use AWS Sagemaker to build an image classification model that can tell bicycles apart from motorcycles. You'll deploy your model, use AWS Lambda functions to build supporting services, and AWS Step Functions to compose your model and services into an event-driven application.
- Step 1: Data staging
- Step 2: Model training and deployment
- Step 3: Lambdas and step function workflow
- Step 4: Testing and evaluation
- Step 5: Cleanup cloud resources
This project was completed as part of the Udacity "Machine Learning Fundamentals" Nanodegree under "AWS AI & ML Scholarship" program.