DEV Community

Shingai Zivuku
Shingai Zivuku

Posted on

AI empowered micro-services: Future application development driven by Spring Boot and Machine Learning


The rapid growth of AI technology has led companies and developers to explore integrating AI into applications to enhance user experience, optimize processes, and innovate business models. Microservice architecture, favored for its flexibility, scalability, and maintainability, pairs well with AI. Combining AI and microservices allows for intelligent services and flexible model deployment. This article introduces building AI-powered microservice applications using Spring Boot and machine learning.

Spring Boot

Spring Boot simplifies the setup and development of Spring applications by providing non-business functions like configuration management, service discovery, and load balancing. It allows developers to focus on implementing business logic and supports the quick creation of standalone, production-grade Spring applications. Spring Boot easily integrates with databases, message queues, and caching systems.

Machine Learning

Machine learning is an important branch of the field of artificial intelligence. It studies how to use calculations to enable computers to learn and make predictions or decisions based on data. Machine learning models usually include steps such as data preprocessing, feature engineering, model training, evaluation and optimization. The trained model can be integrated into applications to provide intelligent recommendations, image recognition, speech recognition, natural language processing and other functions.

Combination of AI and Microservices

Combining AI with microservices can achieve rapid development and deployment of intelligent services. Specifically, machine learning models can be encapsulated into microservices and provided services through REST API or gRPC. This design allows the model to be deployed and upgraded independently of business logic, and also facilitates integration and collaboration with other microservices.

Practical Cases

Suppose we want to develop an e-commerce recommendation system microservice based on Spring Boot and machine learning. Here's a simple example to illustrate how to do this.

Environment preparation

First, we need to prepare the Spring Boot development environment and introduce relevant machine learning libraries, such as TensorFlow or PyTorch's Java bindings.

Create Spring Boot project

Use Spring Initializr to create a new Spring Boot project and add necessary dependencies, such as Spring Web, Spring Data JPA, etc.

Define model service interface

Define an interface in the Spring Boot project to describe the services provided by the machine learning model. For example:

public interface RecommendationService {
    List<Product> recommendProducts(User user);
Enter fullscreen mode Exit fullscreen mode

Implement model services

Implement the above interface, load the trained machine learning model, and provide the function of recommending products. It is assumed here that we already have a trained TensorFlow model and convert it to TensorFlow Lite format for use in Java.

import org.tensorflow.lite.Interpreter;
import org.tensorflow.lite.Tensor;
// ... other necessary imports

public class TensorFlowRecommendationService implements RecommendationService {
    private Interpreter tflite;
    // ... initialize model, load model and other codes

    public List<Product> recommendProducts(User user) {
        // ... Convert user features to model input format
        // ... Call the model for inference
        // ... Convert model output to product list and back
Enter fullscreen mode Exit fullscreen mode

Create REST controller

Create a Spring MVC controller to handle requests from clients and call the model service for recommendations.

import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
// ... Other necessary imports

public class RecommendationController {
    private final RecommendationService recommendationService;

    public RecommendationController(RecommendationService recommendationService) {
        this.recommendationService = recommendationService;

    public ResponseEntity<List<Product>> getRecommendations(@RequestParam String userId) {
        try {
            User user = getUserById(userId); // Suppose there is a method to get user information based on user ID
            List<Product> recommendedProducts = recommendationService.recommendProducts(user);
            return ResponseEntity.ok(recommendedProducts);
        } catch (Exception e) {
            return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body(null);

    private User getUserById(String userId) {
        // Implement the logic of obtaining user information. This is just an example. The specific implementation is based on business needs.
        return new User(userId, "UserName", /* Other user attributes */);
Enter fullscreen mode Exit fullscreen mode

Running and testing

Start the Spring Boot application and use tools such as Postman or curl to send GET requests /api/recommendations?userId=xxxto test the recommended functionality. Ensure that the machine learning model can load correctly and return reasonable recommendation results.


By combining Spring Boot with machine learning technology, we can quickly build powerful microservice applications and realize the development and deployment of intelligent services. This article shows how to integrate Spring Boot and machine learning models to build microservices through a simple e-commerce recommendation system example. In practical applications, more factors need to be considered, such as model performance optimization, service scalability, security, etc. In the future, with the continuous development of AI technology, we can expect more innovations and breakthroughs to appear in the field of microservices.

Top comments (0)