02 Deploy Microservices - Red Hat AI Hackathon Java Code Generation
Read more here https://github.com/smartabyar-smartvi...
Red Hat AI Combinator Hackathon entry
Smart Device API Code Generation with AI/ML hackathon
By Christopher Tate
Because reactive/asynchronous APIs are more efficient and scalable, but are much more complex to build by hand. We can use code generation to build extendable APIs consistently, faster, and more secure with AI/ML Code Generation based on the well established computate open source project. Increase Quarkus API and web developer productivity with OpenAPI Code Generation based on code comments. Easily deploy new edge device models and edge device models following open source FIWARE smart data model schemas with AI/ML predictive code generation.
Have you heard the joke, how microservices were invented? A software engineer had a task to take an existing monolithic software application and split it into microservices. The engineer connected 5 cell phones to the existing application, glued each phone to a picture frame, and showed the application running on all 5 phones. Real microservices run in modern cloud environments like Red Hat OpenShift.
A working cloud project that is capable of receiving data from IoT smart devices and processing edge device data is composed of several important microservices. We will deploy each of the required microservices below to begin sending, receiving, and analyzing IoT edge device data. Then in later notebooks, we will review each microservice and how it works inside of the Smart Village Platform.