要从文件加载机器学习模型，你可以按照以下步骤进行：

1. 首先，确保你已经安装了Apache Flink和相关的机器学习库。

2. 创建一个Flink作业，这个作业将读取包含模型的文件，并将其加载到内存中。

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.core.fs.FileSystem;
import org.apache.flink.ml.common.Model;
import org.apache.flink.ml.common.ParameterMap;
import org.apache.flink.ml.pipeline.PredictOperation;
import org.apache.flink.ml.pipeline.PredictionModel;
import org.apache.flink.ml.pipeline.TransformOperation;
import org.apache.flink.ml.pipeline.TrainedModel;
import org.apache.flink.ml.pipeline.Transformer;
import org.apache.flink.ml.pipeline.UseCase;
import org.apache.flink.ml.pipeline.fit.FitOperation;
import org.apache.flink.ml.pipeline.fit.FittedTransformer;
import org.apache.flink.ml.pipeline.fit.FitOperation;
import org.apache.flink.ml.recommendation.ALS;
import org.apache.flink.ml.recommendation.ALS.Rating;
import org.apache.flink.ml.recommendation.ALS.Rating;
import org.apache.flink.ml.recommendation.ALS.Rating;
import org.apache.flink.ml.recommendation.ALS.Rating;
import org.apache.flink.ml.recommendation.ALS.Rating;
import org.apache.flink.ml.recommendation.ALS.Rating;
import org.apache.flink.ml.recommendation.ALS.Rating;
import org.apache.flink.ml.recommendation.ALS.Rating;
import org.apache.flink.ml.recommendation.ALS.Rating;

public class LoadModelFromFiles {
    public static void main(String[] args) throws Exception {

        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        // 从文件中加载模型
        Model model = Model.load(env, "path/to/model");

        // 使用模型进行预测
        PredictOperation predict = model.transform()
                .select("features")
                .as(new TupleTypeInfo<>(BasicTypeInfo.FLOAT_TYPE_INFO))
                .map(new MapFunction, Float>() {
                    @Override
                    public Float map(Tuple2 value) throws Exception {
                        return value.f0 * value.f1;
                    }
                });

        // 输出预测结果
        predict.writeAsText("path/to/output", FileSystem.WriteMode.OVERWRITE);

        // 执行作业
        env.execute("Load Model From Files");
    }
}

在这个示例中，我们使用了Apache Flink的Model.load()方法来从文件中加载模型。然后，我们使用加载的模型来进行预测，并将预测结果写入文件。

请确保将"path/to/model"替换为实际的模型文件路径，将"path/to/output"替换为实际的输出文件路径。

这是一个简单的示例，你可以根据自己的需求进行更复杂的模型加载和预测操作。