In this module, you will learn how Wide World Importers (WWI) can leverage Deep Learning technologies to scan through their vehicle specification documents to find compliance issues with new regulations and manage the classification thru their web application. The entire process from model creation, application packaging, model deployment and application deployment needs to occur as one unified repeatable, pipeline. At the end of this module, you will be better able to design and implement end-to-end solutions that fully operationalize deep learning models, inclusive of all application components that depend on the model.

In this module, you will learn how Trey Research can leverage Deep Learning technologies to scan through their vehicle specification documents to find compliance issues with new regulations. You will standardize the model format to ONNX and observe how this simplifies inference runtime code, enabling pluggability of different models and targeting a broad range of runtime environments, and most importantly, improves inferencing speed over the native model. You will build a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application, and inferencing web service. After a first successful deployment, you will make updates to both the model, the and web application, and execute the pipeline once to achieve an updated deployment. You will also learn how to monitor the model’s performance after it is deployed, so Trey Research can be proactive with performance issues. At the end of this module, you will be better able to implement end-to-end solutions that fully operationalize deep learning models, inclusive of all application components that depend on the model.