Using Deep Java Library to do Machine Learning on SpringBoot

Many AWS customers--startups and large enterprises--are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to fraud detection and collaboration. However, until recently, the adoption learning curve was steep and required development of internal technical expertise in new programming languages (e.g., Python) and frameworks, with cascading effect on the whole software development lifecycle, from coding to building, testing, and deployment. The approach outlined in this blog post enables enterprises to leverage existing talent and resources (frameworks, pipelines, and deployments) to integrate machine learning capabilities. Spring Boot, one of the most popular and widespread open source frameworks for microservices development, has simplified the implementation of distributed systems.