Integrate ML into Spring Applications with Spring Boot Starter for Deep Java Library: Enhance Microservices with Deep Learning Capabilities
Many AWS customers, ranging from startups to large enterprises, are increasingly adopting machine learning and deep learning technologies within their existing applications. This adoption is driven by the rapid pace of innovation in the industry, addressing diverse business use cases such as customer service enhancements (including object detection from images and video streams, sentiment analysis), fraud detection, and improved collaboration tools.
Historically, the path to machine learning adoption has been fraught with challenges. The steep learning curve often required acquiring new technical skills in programming languages like Python and mastering various frameworks. This requirement had a cascading effect on the entire software development lifecycle, from coding to building, testing, and deployment. This blog post introduces an approach that enables enterprises to leverage their existing talent and resources, including frameworks, pipelines, and deployment mechanisms, to integrate machine learning capabilities more seamlessly.
Introduction
Spring Boot is widely recognized as one of the most popular and effective open-source frameworks for microservices development, greatly simplifying the implementation of distributed systems. However, despite its broad appeal, integrating Spring Boot with machine learning (ML) has traditionally been challenging. Existing solutions, such as stock APIs, often fall short of meeting the specific needs of customized applications, while developing tailored solutions can be both time-consuming and costly.
Developers have explored various methods for integrating machine learning capabilities into their applications. For instance, inference options vary from using stock APIs to creating Python or C++ applications wrapped with an API for remote calls. While stock APIs are built on robust models, they may not always align with specific domain or industry requirements, leading to potential issues discovered only in production. Moreover, when performing inference at scale—such as in streaming applications or latency-sensitive microservices—remote calls may not be feasible due to performance constraints.
To address these challenges, AWS has developed several open-source projects aimed at facilitating machine learning adoption for Java and microservices. These initiatives are designed to make machine learning technology more accessible, aligning with AWS’s mission to democratize advanced technology that was previously cost-prohibitive and complex for many organizations.
In this blog post, we will demonstrate how Java developers can integrate machine learning into their Spring applications using the Spring Boot Starter for Deep Java Library (DJL). We will explore practical applications of these frameworks, showcasing how to incorporate machine learning capabilities into a microservice. Specific use cases will include deep learning applications such as object detection and classification, illustrating the potential of integrating these technologies within a Spring Boot environment.