API World 2018

Using an AI-Driven Model for Self-Service API Integration Developers must often build new applications that pull together multiple back-end services delivered via APIs. A new mobile app, for example, may need to connect to backend ERP or CRM systems using a variety of third-party APIs, which are then integrated and delivered as a new API that provides the mobile app interface. By applying machine learning to this process, developers can greatly increase the speed and reduce the complexity of API integration and management, empowering non-developers with the benefits of API integration. For example, with a machine learning model trained by integration pipeline metadata, analysts across the organization in different lines of businesses with varied skill sets can be presented with recommendations on API integration workflows, to automatically start selecting and assembling APIs to build new applications. This self-service model for API integration greatly increases the speed of new application development while following recommended best practices and reducing security and compliance risks. In this session, Ravi Dharnikota will outline the challenges of API integration in a modern cloud context and explain how developers can leverage machine learning to speed application development, reduce errors and improve security and compliance.