 ```python
import json
import pickle
from fate_arch.session import get_session
from fate_arch.job import Job
from fate_arch.metadata.table import TableMeta
from fate_arch.common.consts import JobType, Party, DataType, FieldType
from fate_flow.settings import FATE_FLOW_HOME
from fate_flow.thirdparty.fate_client.api.flow import FlowAPI
from fate_flow.thirdparty.fate_client.api.table import TableAPI
from fate_flow.thirdparty.fate_client.entity.table import Table
from fate_flow.thirdparty.fate_client.entity.flow import Flow
from fate_flow.thirdparty.fate_client.entity.table_filter import TableFilter
from fate_flow.thirdparty.fate_client.entity.flow_filter import FlowFilter
from fate_flow.thirdparty.fate_client.entity.role import Role
from fate_flow.thirdparty.fate_client.entity.job_param import JobParam
from fate_flow.thirdparty.fate_client.entity.table_filter import TableFilter
from fate_flow.thirdparty.fate_client.entity.flow_filter import FlowFilter
from fate_flow.thirdparty.fate_client.entity.role import Role
from fate_flow.thirdparty.fate_client.api.flow import FlowAPI
from fate_flow.thirdparty.fate_client.api.table import TableAPI
from fate_flow.thirdparty.fate_client.api.component import ComponentAPI
from fate_flow.thirdparty.fate_client.api.job import JobAPI
from fate_flow.thirdparty.fate_client.api.role import RoleAPI
from fate_flow.thirdparty.fate_client.api.process import ProcessAPI
from fate_flow.thirdparty.fate_client.api.model import ModelAPI
from fate_flow.thirdparty.fate_client.api.storage import StorageAPI
from fate_flow.thirdparty.fate_client.api.component_instance import ComponentInstanceAPI
from fate_flow.thirdparty.fate_client.api.model_template import ModelTemplateAPI
from fate_flow.thirdparty.fate_client.api.model_version import ModelVersionAPI
from fate_flow.thirdparty.fate_client.api.model_deploy import ModelDeployAPI
from fate_flow.thirdparty.fate_client.api.model_summary import ModelSummaryAPI

def create_data_reader(table_name):
    return {
        "component_id": "component_data_reader",
        "component_params": {
            "table_name": table_name,
            "data_type": DataType.HETERO_DATA,
            "fields": [
                {
                    "name": "feature_1",
                    "type": FieldType.FLOAT,
                    "is_feature": True
                },
                {
                    "name": "feature_2",
                    "type": FieldType.FLOAT,
                    "is_feature": True
                },
                {
                    "name": "label",
                    "type": FieldType.FLOAT,
                    "is_feature": False
                }
            ]
        }
    }

def create_data_transformer():
    return {
        "component_id": "component_data_transformer",
        "component_params": {
            "transformation_params": {
                "columns": ["feature_1", "feature_2"],
                "operations": [
                    {
                        "operation": "Normalize",
                        "parameters": {
                            "mean": 0.0,
                            "std": 1.0
                        }
                    }
                ]
            }
        }
    }

def create_feature_scaler():
    return {
        "component_id": "component_feature_scaler",
        "component_params": {
            "scaling_params": {
                "columns": ["feature_1", "feature_2"],
                "method": "StandardScaler"
            }
        }
    }

def create_logistic_regression_model():
    return {
        "component_id": "component_logistic_regression",
        "component_params": {
            "lr_params": {
                "penalty": "l2",
                "optimizer": "sgd",
                "tol": 1e-4,
                "alpha": 0.01,
                "max_iter": 100,
                "early_stopping": True,
                "batch_size": 32,
                "learning_rate": 0.01,
                "decay": 0.0,
                "init_method": "xavier",
                "cv": 5
            }
        }
    }

def create_evaluator():
    return {
        "component_id": "component_evaluator",
        "component_params": {
            "evaluation_params": {
                "metrics": ["accuracy", "precision", "recall", "f1"]
            }
        }
    }

def create_pipeline(data_reader, data_transformer, feature_scaler, logistic_regression_model, evaluator):
    return {
        "pipeline_id": "pipeline_logistic_regression",
        "pipeline_params": {
            "components": [
                data_reader,
                data_transformer,
                feature_scaler,
                logistic_regression_model,
                evaluator
            ],
            "dependencies": [
                ["component_data_reader", "component_data_transformer"],
                ["component_data_transformer", "component_feature_scaler"],
                ["component_feature_scaler", "component_logistic_regression"],
                ["component_logistic_regression", "component_evaluator"]
            ]
        }
    }

def compile_pipeline(pipeline):
    session = get_session()
    pipeline_config = PipelineConfig(**pipeline)
    pipeline_job = PipelineJob(pipeline_config)
    compiled_pipeline = session.compile(pipeline_job)
    return compiled_pipeline

def fit_pipeline(compiled_pipeline):
    session = get_session()
    fitted_pipeline = session.fit(compiled_pipeline)
    return fitted_pipeline

def deploy_components(fitted_pipeline, components):
    session = get_session()
    deployed_components = session.deploy(fitted_pipeline, components)
    return deployed_components

def create_prediction_pipeline(data_reader, deployed_components):
    return {
        "pipeline_id": "pipeline_prediction",
        "pipeline_params": {
            "components": [
                data_reader,
                *deployed_components
            ],
            "dependencies": [
                [deployed_components[0], deployed_components[1]],
                [deployed_components[1], deployed_components[2]],
                [deployed_components[2], deployed_components[3]],
                [deployed_components[3], deployed_components[4]]
            ]
        }
    }

def compile_prediction_pipeline(prediction_pipeline):
    session = get_session()
    prediction_pipeline_config = PipelineConfig(**prediction_pipeline)
    prediction_pipeline_job = PipelineJob(prediction_pipeline_config)
    compiled_prediction_pipeline = session.compile(prediction_pipeline_job)
    return compiled_prediction_pipeline

def make_prediction(compiled_prediction_pipeline, input_data):
    session = get_session()
    prediction = session.predict(compiled_prediction_pipeline, input_data)
    return prediction

def save_pipeline_dsl(pipeline_dsl, file_path):
    with open(file_path, "w") as f:
        json.dump(pipeline_dsl, f)

def save_pipeline_configuration(pipeline_configuration, file_path):
    with open(file_path, "w") as f:
        json.dump(pipeline_configuration, f)

def print_model_summary(model_summary):
    print(json.dumps(model_summary, indent=4))

def main(config_file):
    config = # load configuration from file

    data_reader = create_data_reader(config["data