Machines Can Learn - a Practical Take on MI Using Spring Cloud Data Flow and TensorFlow
Machine learning (ML) has brought unprecedented abilities to the software engineering field. ML allows you to reason about and to solve otherwise "un-programmable" tasks such as computer vision and language processing. If you're a Java developer and you're interested in leveraging ML to deliver richer business insights to your customers, in this talk you'll learn what it takes to build cloud-native applications to perform data-driven machine intelligence operations. This coding-centric talk walks through the different facets of iterative development and testing using Spring Cloud Stream and the orchestration of such applications into coherent data pipelines using Spring Cloud Data Flow. Specifically, we will also review TensorFlow, a popular Machine Learning toolkit, and how it is integrated in the overall design. This talk will showcase how building a complex use-cases such as real-time image recognition or object detection, can be simplified with the help of Spring Ecosystem and TensorFlow. More importantly, I'd will share the findings from the ML space; tips and tricks on what goes into developing such complex solutions. Speakers: Christian Tzolov Software Craftsman, Pivotal Filmed at SpringOne Platform 2018
