Wednesday, September 15, 2021
As businesses continue to expose information through APIs, API programming has grown significantly and the number of API endpoints available has increased by leaps and bounds. An integration consuming a set of such APIs needs to adhere to the agreed SLAs with the customers. In the era of cloud integration platforms, making sure that your cloud-based integration is up to scratch in performance is not simple.
Integration-based development increases the risk of performance mistakes in the code compared to traditional programs that do not depend on external services. Since developers combine multiple services or APIs with unknown performance characteristics, these mistakes are usually missed out during development. With the help of artificial intelligence (AI), integrated development environments (IDE) can take up the burden of helping engineers to write performant code.
In this session, I will talk about how to use both AI and theoretical performance models to provide accurate performance forecasts for API integrations. I will demonstrate how this approach can be useful for inexperienced developers to write performant code.