java框架如何支持云原生应用的机器学习和人工智能集成？

import org.apache.spark.sql.SparkSession;
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;

public class MLIntegrationExample {

    public static void main(String[] args) {
        SparkSession spark = SparkSession.builder().appName("ML Integration Example").getOrCreate();
        // 加载数据
        RDD<LabeledPoint> data = MLUtils.loadLibSVMFile(spark.sparkContext(), "data.txt");
        // 划分训练集和测试集
        RDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        RDD<LabeledPoint> trainingData = splits[0];
        RDD<LabeledPoint> testData = splits[1];
        // 训练逻辑回归模型
        LogisticRegressionWithLBFGS model = new LogisticRegressionWithLBFGS();
        model.train(trainingData);
        // 使用模型进行预测
        RDD<Double> predictions = model.predict(testData.map(LabeledPoint::features));
        // 评估模型性能
        double accuracy = predictions.zip(testData.map(LabeledPoint::label)).filter(pair -> pair._1 == pair._2).count() /
                (double) testData.count();
        System.out.println("Accuracy: " + accuracy);
    }
}