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. 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 Scones Unlimited optimize their operations.
As an MLE, our goal is to ship a scalable and safe model. Once our model becomes available to other teams on-demand, it’s important that our model can scale to meet demand, and that safeguards are in place to monitor and control for drift or degraded performance.
In this project, we’ll use AWS Sagemaker to build an image classification model that can tell bicycles apart from motorcycles. We'll deploy your model, use AWS Lambda functions to build supporting services, and AWS Step Functions to compose our model and services into an event-driven application. At the end of this project, we will have created a portfolio-ready demo that showcases our ability to build and compose scalable, ML-enabled, AWS applications.
Project Steps Overview Step 1: Data staging Step 2: Model training and deployment Step 3: Lambdas and step function workflow Step 4: Testing and evaluation Step 5: Optional challenge Step 6: Cleanup cloud resources
I have uploaded all the necessary code for creatinf lambda functions as well as step functions.Hope this helps
