Machine Predictive Upkeep with MLOps

Machines don’t break down out of nowhere—there are at all times indicators. The issue? People aren’t at all times nice at noticing them. That’s the place Machine Predictive Upkeep is available in! This information will take you thru the thrilling world of Machine Predictive Upkeep, utilizing AWS and MLOps to make sure your gear stays predictably dependable.

Studying Goals

• Perceive tips on how to design and implement an end-to-end MLOps pipeline for predictive upkeep, overlaying information ingestion, mannequin coaching, and deployment.
• Study to combine important instruments like Docker, FastAPI, and AWS providers to construct a strong, production-ready machine studying software.
• Discover using GitHub Actions for automating CI/CD workflows, guaranteeing easy and dependable code integration and deployment.
• Arrange finest practices for monitoring, efficiency monitoring, and steady enchancment to maintain your machine studying fashions environment friendly and maintainable.

This text was printed as part of the Knowledge Science Blogathon.

Downside: Unplanned Downtime & Upkeep Prices

Surprising gear failures in industrial settings trigger downtime and monetary losses. In our challenge, we’re utilizing MLOps finest practices and machine studying to detect points early, enabling well timed repairs and decreasing disruptions.

Earlier than diving into implementation, let’s take a better have a look at the challenge structure.

Needed Stipulations

Under we are going to first look within the stipulations required:

Clone the repository:

git clone "https://github.com/karthikponna/Predictive_Maintenance_MLOps.git"
cd Predictive_Maintenance_MLOps

Create and activate the digital atmosphere:

# For macOS and Linux:
python3 -m venv venv
supply venv/bin/activate

# For Home windows:
python -m venv venv
.venvScriptsactivate

Set up Required Dependencies:

pip set up -r necessities.txt

Set Up Atmosphere Variables:

# Create a `.env` file and add your MongoDB connection string:
MONGO_URI=your_mongodb_connection_string

Undertaking Construction

The Undertaking Construction outlines the important thing parts and group of the challenge, guaranteeing readability and maintainability. It helps in understanding how completely different modules work together and the way the general system is designed. A well-defined construction simplifies improvement, debugging, and scalability.

project_root/
│
├── .github/
│   └── workflows/
│       └── predominant.yml
│
├── data_schema/
│   └── schema.yaml
│
├── final_model/
│   ├── mannequin.pkl
│   └── preprocessor.pkl
│
├── Machine_Predictive_Data/
│   └── predictive_maintenance.csv
│
├── machine_predictive_maintenance/
│   ├── cloud/
│   ├── parts/
│   ├── fixed/
│   ├── entity/
│   ├── exception/
│   ├── logging/
│   ├── pipeline/
│   ├── utils/
│   └── __init__.py
│
├── my_venv/
│
├── notebooks/
│   ├── EDA.ipynb
│   ├── prediction_output/
│
├── templates/
│   └── desk.html
│
├── valid_data/
|    └──take a look at.csv
│
├── .env
├── .gitignore
├── app.py
├── Dockerfile
├── predominant.py
├── push_data.py
├── README.md
├── necessities.txt
├── setup.py
├── test_mongodb.py

Knowledge Ingestion

On this challenge, we use a machine predictive upkeep CSV file, changing it into JSON information, and inserting it right into a MongoDB assortment. 

Dataset Hyperlink: https://www.kaggle.com/datasets/shivamb/machine-predictive-maintenance-classification

Right here is the code snippet to transform CSV -> JSON information -> MongoDB

class PredictiveDataExtract():

    def __init__(self):
        """
        Initializes the PredictiveDataExtract class.
        """
        strive:
            go
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
    def csv_to_json_convertor(self, file_path):
        strive:
            information = pd.read_csv(file_path)
            information.reset_index(drop=True, inplace=True)
            information = listing(json.hundreds(information.T.to_json()).values())
            return information
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
    def insert_data_mongodb(self, information, database, assortment):
        strive:
            self.information = information
            self.database = database
            self.assortment = assortment

            self.mongo_client = pymongo.MongoClient(MONGO_DB_URL)
            self.database = self.mongo_client[self.database]
            
            self.assortment = self.database[self.collection]
            self.assortment.insert_many(self.information)
            return(len(self.information))
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
if __name__=="__main__":
    FILE_PATH="Machine_Predictive_Data/predictive_maintenance.csv"
    DATABASE="Predictive_Maintenance_MLOps"
    assortment = "Machine_Predictive_Data"

    predictive_data_obj=PredictiveDataExtract()
    information = predictive_data_obj.csv_to_json_convertor(FILE_PATH)
    no_of_records = predictive_data_obj.insert_data_mongodb(information, DATABASE, assortment)
    print(no_of_records)

Right here is the code snippet to fetch information from MongoDB, break up the info into prepare and take a look at CSV information, and retailer them as a Knowledge Ingestion artifact.

class DataIngestion:
    def __init__(self, data_ingestion_config:DataIngestionConfig):

        """
        Initializes the DataIngestion class with the offered configuration.

        Parameters:
            data_ingestion_config: DataIngestionConfig
                Configuration object containing particulars for information ingestion.
        """
        strive:
            self.data_ingestion_config=data_ingestion_config
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
    def export_collection_as_dataframe(self):
        strive:
            database_name= self.data_ingestion_config.database_name
            collection_name= self.data_ingestion_config.collection_name
            self.mongo_client = pymongo.MongoClient(MONGO_DB_URL)
            assortment = self.mongo_client[database_name][collection_name]
            df = pd.DataFrame(listing(assortment.discover()))
            if "_id" in df.columns.to_list():
                df = df.drop(columns=["_id"], axis=1)
            df.substitute({"na":np.nan},inplace=True)
            return df
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)

    def export_data_into_feature_store(self,dataframe: pd.DataFrame):
        strive:
            feature_store_file_path=self.data_ingestion_config.feature_store_file_path
            #creating folder
            dir_path = os.path.dirname(feature_store_file_path)
            os.makedirs(dir_path,exist_ok=True)
            dataframe.to_csv(feature_store_file_path,index=False,header=True)
            return dataframe
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e,sys)

    def split_data_as_train_test(self, dataframe:pd.DataFrame):        
        strive:
            train_set, test_set = train_test_split(
                dataframe, test_size=self.data_ingestion_config.train_test_split_ratio
            )
            logging.data("Carried out prepare take a look at break up on the dataframe")
            logging.data(
                "Exited split_data_as_train_test methodology of Data_Ingestion class"
            )
            dir_path = os.path.dirname(self.data_ingestion_config.training_file_path)
            os.makedirs(dir_path, exist_ok=True)
            logging.data(f"Exporting prepare and take a look at file path.")
            train_set.to_csv(
                self.data_ingestion_config.training_file_path, index=False, header=True
            )
            test_set.to_csv(
                self.data_ingestion_config.testing_file_path, index=False, header=True
                            )
            logging.data(f"Exported prepare and take a look at file path."  )
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)

    def initiate_data_ingestion(self):
        strive:
            dataframe = self.export_collection_as_dataframe()
            dataframe = self.export_data_into_feature_store(dataframe)
            self.split_data_as_train_test(dataframe)
            dataingestionartifact= DataIngestionArtifact(trained_file_path=self.data_ingestion_config.training_file_path,
                                                         test_file_path=self.data_ingestion_config.testing_file_path)
            return dataingestionartifact
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e,sys)

Knowledge Validation

On this step, we’ll test if the ingested information meets the anticipated format by guaranteeing all required columns are current utilizing a predefined schema and evaluating the coaching and testing information for any variations. Then we save the clear information and create a drift report, so solely high quality information is used for the following step, which is remodeling the info. 

Knowledge Validation code snippet:

class DataValidation:

    def __init__(self, data_ingestion_artifact:DataIngestionArtifact,
                 data_validation_config: DataValidationConfig):
        strive:
            self.data_ingestion_artifact= data_ingestion_artifact
            self.data_validation_config= data_validation_config
            self._schema_config = read_yaml_file(SCHEMA_FILE_PATH)
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
    @staticmethod
    def read_data(file_path) -> pd.DataFrame:
        strive:
            return pd.read_csv(file_path)
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys) 

    
    def validate_number_of_columns(self, dataframe:pd.DataFrame)-> bool:
        strive:
            number_of_columns = len(self._schema_config)
            logging.data(f"Required variety of columns:{number_of_columns}")
            logging.data(f"Knowledge body has columns:{len(dataframe.columns)}")

            if len(dataframe.columns) == number_of_columns: 
                return True
            return False
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException (e, sys)
        

    def is_columns_exist(self, df:pd.DataFrame) -> bool:
        strive:
            dataframe_columns = df.columns
            missing_numerical_columns = []
            missing_categorical_columns = []

            for column in self._schema_config["numerical_columns"]:
                if column not in dataframe_columns:
                    missing_numerical_columns.append(column)

            if len(missing_numerical_columns) > 0:
                logging.data(f"Lacking numerical column: {missing_numerical_columns}")

            for column in self._schema_config["categorical_columns"]:
                if column not in dataframe_columns:
                    missing_categorical_columns.append(column)

            if len(missing_categorical_columns) > 0:
                logging.data(f"Lacking categorical column: {missing_categorical_columns}")
            return False if len(missing_categorical_columns)>0 or len(missing_numerical_columns)>0 else True
            
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)

    
    def detect_dataset_drift(self, base_df, current_df, threshold = 0.05) -> bool :
        strive:
            standing = True
            report = {}
            for column in base_df.columns:
                d1 = base_df[column]
                d2 = current_df[column]

                is_same_dist = ks_2samp(d1, d2)
                if threshold <= is_same_dist.pvalue:
                    is_found = False

                else: 
                    is_found = True
                    standing = False

                report.replace({column:{
                    "p_value": float(is_same_dist.pvalue),
                    "drift_status": is_found
                }})
            drift_report_file_path = self.data_validation_config.drift_report_file_path
            dir_path = os.path.dirname(drift_report_file_path)# create listing
            os.makedirs(dir_path, exist_ok=True)

            write_yaml_file(file_path=drift_report_file_path, content material=report)
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
    def initiate_data_validation(self)-> DataValidationArtifact:
        strive:
            validation_error_msg  = ""
            logging.data("Beginning information validation")

            train_file_path = self.data_ingestion_artifact.trained_file_path
            test_file_path = self.data_ingestion_artifact.test_file_path

            # learn the info from the prepare and take a look at 
            train_dataframe = DataValidation.read_data(train_file_path)
            test_dataframe = DataValidation.read_data(test_file_path)

            # validate variety of columns
            standing = self.validate_number_of_columns(dataframe=train_dataframe)
            logging.data(f"All required columns current in coaching dataframe: {standing}")

            if not standing:
                validation_error_msg += f"Prepare dataframe doesn't include all columns.n"

            standing = self.validate_number_of_columns(dataframe=test_dataframe)

            if not standing:
                validation_error_msg += f"Take a look at dataframe doesn't include all columns.n"

            standing = self.is_columns_exist(df=train_dataframe)

            if not standing:
                validation_error_msg += f"Columns are lacking in coaching dataframe."

            standing = self.is_columns_exist(df=test_dataframe)

            if not standing:
                validation_error_msg += f"columns are lacking in take a look at dataframe."
            ## lets test datadrift
            standing=self.detect_dataset_drift(base_df=train_dataframe,current_df=test_dataframe)
            dir_path=os.path.dirname(self.data_validation_config.valid_train_file_path)
            os.makedirs(dir_path,exist_ok=True)

            train_dataframe.to_csv(
                self.data_validation_config.valid_train_file_path, index=False, header=True

            )
            test_dataframe.to_csv(
                self.data_validation_config.valid_test_file_path, index=False, header=True
            )

            data_validation_artifact = DataValidationArtifact(
                validation_status=standing,
                valid_train_file_path=self.data_validation_config.valid_train_file_path,
                valid_test_file_path=self.data_validation_config.valid_test_file_path,
                invalid_train_file_path=None,
                invalid_test_file_path=None,
                drift_report_file_path=self.data_validation_config.drift_report_file_path,
            )
            return data_validation_artifact
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e,sys)

Under is the drift report generated by the Knowledge Validation.

Air temperature [K]:
  drift_status: true
  p_value: 0.016622943467175914
Failure Kind:
  drift_status: false
  p_value: 1.0
Course of temperature [K]:
  drift_status: false
  p_value: 0.052940072765804994
Product ID:
  drift_status: false
  p_value: 0.09120557172716418
Rotational velocity [rpm]:
  drift_status: false
  p_value: 0.2673520066245566
Goal:
  drift_status: false
  p_value: 0.999999998717466
Software put on [min]:
  drift_status: false
  p_value: 0.13090856779628832
Torque [Nm]:
  drift_status: false
  p_value: 0.5001773464540389
Kind:
  drift_status: false
  p_value: 1.0
UDI:
  drift_status: true
  p_value: 0.022542489133976953

Knowledge Transformation

Right here, we are going to clear and rework the validated information by changing temperature options from Kelvin to Celsius, dropping pointless columns, and making use of a change pipeline that makes use of ordinal encoding and Min-Max scaling, and dealing with information imbalance utilizing SMOTEENN.

The reworked coaching and take a look at datasets are saved as .npy information together with a serialized preprocessing object i.e. Minmax scaler(preprocessing.pkl) all encapsulated as an artifact for additional mannequin coaching.

class DataTransformation:

    def __init__(self,data_validation_artifact: DataValidationArtifact,
                 data_transformation_config: DataTransformationConfig):
        strive:
            self.data_validation_artifact: DataValidationArtifact = data_validation_artifact
            self.data_transformation_config: DataTransformationConfig = data_transformation_config
            self._schema_config = read_yaml_file(file_path=SCHEMA_FILE_PATH)
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e,sys)
        
    @staticmethod
    def read_data(file_path) -> pd.DataFrame:
        strive:
            return pd.read_csv(file_path)
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
    def get_data_transformer_object(self):
        strive:
            logging.data("Acquired numerical cols from schema config")
            
            scaler = MinMaxScaler()
            # Fetching classes for OrdinalEncoder from schema config
            ordinal_categories = self._schema_config.get('ordinal_categories', [])
            ordinal_encoder = OrdinalEncoder(classes=ordinal_categories)

            logging.data("Initialized MinMaxScaler, OrdinalEncoder with classes")

            ordinal_columns = self._schema_config['ordinal_columns']
            scaling_features = self._schema_config['scaling_features']
            preprocessor = ColumnTransformer(
                [
                    ("Ordinal_Encoder", ordinal_encoder, ordinal_columns),
                    ("MinMaxScaling", scaler, scaling_features)
                ]
            )
            logging.data("Created preprocessor object from ColumnTransformer")
            return preprocessor
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
    def initiate_data_transformation(self) -> DataTransformationArtifact:
        strive:
            
            logging.data("Beginning information transformation")
            preprocessor = self.get_data_transformer_object()
            train_df = DataTransformation.read_data(self.data_validation_artifact.valid_train_file_path)
            test_df = DataTransformation.read_data(self.data_validation_artifact.valid_test_file_path)
            input_feature_train_df = train_df.drop(columns=[TARGET_COLUMN], axis=1)
            target_feature_train_df = train_df[TARGET_COLUMN]

            input_feature_train_df['Air temperature [c]'] = input_feature_train_df['Air temperature [K]'] - 273.15
            input_feature_train_df['Process temperature [c]'] = input_feature_train_df['Process temperature [K]'] - 273.15
            drop_cols = self._schema_config['drop_columns']

            input_feature_train_df = drop_columns(df=input_feature_train_df, cols = drop_cols)
            logging.data("Accomplished dropping the columns for Coaching dataset")
            input_feature_test_df = test_df.drop(columns=[TARGET_COLUMN], axis=1)
            target_feature_test_df = test_df[TARGET_COLUMN]

            input_feature_test_df['Air temperature [c]'] = input_feature_test_df['Air temperature [K]'] - 273.15
            input_feature_test_df['Process temperature [c]'] = input_feature_test_df['Process temperature [K]'] - 273.15
            drop_cols = self._schema_config['drop_columns']
            input_feature_test_df = drop_columns(df=input_feature_test_df, cols = drop_cols)

            logging.data("Accomplished dropping the columns for Testing dataset")
            input_feature_train_arr = preprocessor.fit_transform(input_feature_train_df)
            input_feature_test_arr = preprocessor.rework(input_feature_test_df)
            
            smt =  SMOTEENN(sampling_strategy="minority")
            input_feature_train_final, target_feature_train_final = smt.fit_resample(
                input_feature_train_arr, target_feature_train_df
            )
            logging.data("Utilized SMOTEENN on coaching dataset")

            input_feature_test_final, target_feature_test_final = smt.fit_resample(
                input_feature_test_arr, target_feature_test_df
            )
            train_arr = np.c_[
                input_feature_train_final, np.array(target_feature_train_final)
            ]
            test_arr = np.c_[
                input_feature_test_final, np.array(target_feature_test_final)
            ]
            save_numpy_array_data(self.data_transformation_config.transformed_train_file_path, array=train_arr, )
            save_numpy_array_data(self.data_transformation_config.transformed_test_file_path,array=test_arr,)
            save_object( self.data_transformation_config.transformed_object_file_path, preprocessor,)

            save_object( "final_model/preprocessor.pkl", preprocessor,)

            data_transformation_artifact=DataTransformationArtifact(
                transformed_object_file_path=self.data_transformation_config.transformed_object_file_path,
                transformed_train_file_path=self.data_transformation_config.transformed_train_file_path,
                transformed_test_file_path=self.data_transformation_config.transformed_test_file_path
            )
            return data_transformation_artifact
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)

Coaching and Analysis

Now we are going to prepare completely different classification fashions on the reworked coaching information like Choice Tree, Random Forest, Gradient Boosting, Logistic Regression, and AdaBoost fashions and consider their efficiency utilizing analysis metrics like f1-score, precision, and recall, after which log these particulars with MLflow.

After selecting the right mannequin, it saves each the mannequin and transformation object regionally to artifact and analysis metrics to make use of it for deployment.

class ModelTrainer:
    def __init__(self, data_transformation_artifact: DataTransformationArtifact, model_trainer_config: ModelTrainerConfig):
        strive:
            self.data_transformation_artifact = data_transformation_artifact
            self.model_trainer_config = model_trainer_config

        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
        
    def track_mlflow(self, best_model, classification_metric, input_example):
        """
        Log mannequin and metrics to MLflow.

        Args:
            best_model: The educated mannequin object.
            classification_metric: The classification metrics (f1, precision, recall).
            input_example: An instance enter information pattern for the mannequin.
        """
        strive:
            with mlflow.start_run():
                f1_score=classification_metric.f1_score
                precision_score=classification_metric.precision_score
                recall_score=classification_metric.recall_score

                mlflow.log_metric("f1_score",f1_score)
                mlflow.log_metric("precision",precision_score)
                mlflow.log_metric("recall_score",recall_score)
                mlflow.sklearn.log_model(best_model,"mannequin", input_example=input_example)
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)
    
    def train_model(self, X_train, y_train, X_test, y_test ):
        """
        Prepare a number of fashions and choose the best-performing one primarily based on analysis metrics.

        Args:
            X_train: Coaching options.
            y_train: Coaching labels.
            X_test: Testing options.
            y_test: Testing labels.

        Returns:
            ModelTrainerArtifact: An artifact containing particulars concerning the educated mannequin and its metrics.
        """
        fashions = {
                "Random Forest": RandomForestClassifier(verbose=1),
                "Choice Tree": DecisionTreeClassifier(),
                "Gradient Boosting": GradientBoostingClassifier(verbose=1),
                "Logistic Regression": LogisticRegression(verbose=1),
                "AdaBoost": AdaBoostClassifier(),
            }
        params={
            "Choice Tree": {
                'criterion':['gini', 'entropy', 'log_loss'],
                # 'splitter':['best','random'],
                # 'max_features':['sqrt','log2'],
            },
            "Random Forest":{
                # 'criterion':['gini', 'entropy', 'log_loss'],
                
                # 'max_features':['sqrt','log2',None],
                'n_estimators': [8,16,32,128,256]
            },
            "Gradient Boosting":{
                # 'loss':['log_loss', 'exponential'],
                'learning_rate':[.1,.01,.05,.001],
                'subsample':[0.6,0.7,0.75,0.85,0.9],
                # 'criterion':['squared_error', 'friedman_mse'],
                # 'max_features':['auto','sqrt','log2'],
                'n_estimators': [8,16,32,64,128,256]
            },
            "Logistic Regression":{},
            "AdaBoost":{
                'learning_rate':[.1,.01,.001],
                'n_estimators': [8,16,32,64,128,256]
            }
            
        }
        model_report: dict = evaluate_models(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test,
                                             fashions=fashions, param=params)
        best_model_score = max(sorted(model_report.values()))
        logging.data(f"Finest Mannequin Rating: {best_model_score}")
        best_model_name = listing(model_report.keys())[
            list(model_report.values()).index(best_model_score)
        ]
        logging.data(f"Finest Mannequin Title: {best_model_name}")
        best_model = fashions[best_model_name]

        y_train_pred = best_model.predict(X_train)
        classification_train_metric = get_classification_score(y_true=y_train, y_pred=y_train_pred)

        input_example = X_train[:1]
        print(input_example)
        # Observe the experiments with mlflow
        self.track_mlflow(best_model, classification_train_metric, input_example)


        y_test_pred=best_model.predict(X_test)
        classification_test_metric = get_classification_score(y_true=y_test, y_pred=y_test_pred)

        # Observe the experiments with mlflow
        self.track_mlflow(best_model, classification_test_metric, input_example)


        preprocessor = load_object(file_path=self.data_transformation_artifact.transformed_object_file_path)


        model_dir_path = os.path.dirname(self.model_trainer_config.trained_model_file_path)
        os.makedirs(model_dir_path,exist_ok=True)

        Machine_Predictive_Model = MachinePredictiveModel(mannequin=best_model)

        save_object(self.model_trainer_config.trained_model_file_path,obj=MachinePredictiveModel)

        save_object("final_model/mannequin.pkl",best_model)

        ## Mannequin Coach Artifact
        model_trainer_artifact=ModelTrainerArtifact(trained_model_file_path=self.model_trainer_config.trained_model_file_path,
                             train_metric_artifact=classification_train_metric,
                             test_metric_artifact=classification_test_metric
                             )
        logging.data(f"Mannequin coach artifact: {model_trainer_artifact}")
        return model_trainer_artifact

    def initiate_model_trainer(self) -> ModelTrainerArtifact:

        strive:
            train_file_path = self.data_transformation_artifact.transformed_train_file_path
            test_file_path = self.data_transformation_artifact.transformed_test_file_path

            #loading coaching array and testing array
            train_arr = load_numpy_array_data(train_file_path)
            test_arr = load_numpy_array_data(test_file_path)

            X_train, y_train, X_test, y_test = (
                train_arr[:, :-1],
                train_arr[:, -1],
                test_arr[:, :-1],
                test_arr[:, -1],
            )

            model_trainer_artifact=self.train_model(X_train,y_train,X_test,y_test)
            return model_trainer_artifact
            
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e, sys)

Now, let’s create a training_pipeline.py file the place we sequentially combine all of the steps of information ingestion, validation, transformation, and mannequin coaching into a whole pipeline.

def run_pipeline(self):

        """
        Executes the complete coaching pipeline.

        Returns:
            ModelTrainerArtifact: Incorporates metadata concerning the educated mannequin.
        """
        strive:
            data_ingestion_artifact= self.data_ingestion()
            data_validation_artifact= self.data_validation(data_ingestion_artifact=data_ingestion_artifact)
            data_transformation_artifact= self.data_transformation(data_validation_artifact=data_validation_artifact)
            model_trainer_artifact= self.model_trainer(data_transformation_artifact=data_transformation_artifact)
            self.sync_artifact_dir_to_s3()
            self.sync_saved_model_dir_to_s3()
            
            return model_trainer_artifact
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e,sys)

You may visually see how we’ve got constructed the training_pipeline.

Now that we’ve accomplished creating the pipeline, run the training_pipeline.py file to view the artifacts generated within the earlier steps.

python training_pipeline.py

Run the next command to view the MLflow dashboard.

mlflow ui  # Launch the MLflow dashboard to observe experiments.

As you’ll be able to see, we’ve got efficiently logged mannequin metrics like recall, precision, and F1-score in MLflow.

AWS Integration

After working the training_pipeline.py file, we generated artifacts and saved them regionally. Now, we are going to retailer these artifacts in an AWS S3 bucket to allow cloud-based storage and accessibility.

Utilizing a Docker picture, we are going to push it to AWS ECR through GitHub Actions after which deploy it to manufacturing utilizing AWS EC2. We’ll talk about this course of in additional element within the upcoming sections.

AWS S3

Comply with these steps to create an AWS S3 bucket:

Step1: Obtain AWS CLI

• You may click on right here to obtain AWS CLI for Home windows, Linux, and macOS

Step2: Log in to AWS IAM Consumer

• You may sign up to the Console together with your IAM person credentials and choose the suitable AWS area (eg., us-east-1).
• As soon as logged in, seek for ‘IAM’ within the AWS search bar. Navigate to ‘Customers,’ choose your username, and go to the ‘Safety Credentials’ tab. Beneath ‘Entry Keys,’ click on ‘Create entry key,’ select ‘CLI,’ after which affirm by clicking ‘Create entry key’.
• Now, open your terminal and sort aws configure. Enter your AWS Entry Key and Secret Entry Key when prompted, then press Enter.

Your IAM person has been efficiently linked to your challenge.

Step3: Navigate to S3 Service

• As soon as logged in, seek for “S3” within the AWS search bar.
• Click on on Amazon S3 service and choose the “Create bucket” button. Subsequent, sort in a singular bucket identify (eg., machinepredictive)
• Evaluate the configuration settings, after which click on on “Create bucket.”

Your bucket is now created, and you can begin importing artifacts. Now, add the next code to your training_pipeline.py file and run it once more to see the artifacts in your AWS S3 bucket.

    def sync_artifact_dir_to_s3(self):

        """
        Syncs the artifact listing to S3.
        """
        strive:

            aws_bucket_url = f"s3://{TRAINING_BUCKET_NAME}/artifact/{self.training_pipeline_config.timestamp}"
            self.s3_sync.sync_folder_to_s3(folder = self.training_pipeline_config.artifact_dir,aws_bucket_url=aws_bucket_url)

        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e,sys)
        
    def sync_saved_model_dir_to_s3(self):

        """
        Syncs the saved mannequin listing to S3.
        """

        strive:
            aws_bucket_url = f"s3://{TRAINING_BUCKET_NAME}/final_model/{self.training_pipeline_config.timestamp}"
            self.s3_sync.sync_folder_to_s3(folder = self.training_pipeline_config.model_dir,aws_bucket_url=aws_bucket_url)
        besides Exception as e:
            elevate MachinePredictiveMaintenanceException(e,sys)

Amazon Elastic Container Registry (ECR)

Comply with these steps to create an ECR repository.

• Seek for ‘ECR’ within the AWS search bar.
• Click on Create Repository, and choose Non-public Repository.
• Present a repository identify (e.g., machinepredictive) and click on Create.

Copy the URI out of your ECR repository, which ought to look one thing like “788614365622.dkr.ecr.ap-southeast-2.amazonaws.com“, and reserve it someplace. We’ll want it later to stick into GitHub Secrets and techniques.

Docker Integration for Deployment

Dockerizing our challenge ensures it runs easily in any atmosphere with out dependency points. It’s a must have device for packaging and sharing purposes effortlessly.

Holding the Docker picture as small as attainable is vital for effectivity. We will reduce its measurement through the use of strategies like multi-staging and selecting a light-weight Python base picture.

FROM python:3.10-slim-buster
WORKDIR /app
COPY . /app

RUN apt replace -y && apt set up awscli -y

RUN apt-get replace && pip set up -r necessities.txt
CMD ["python3", "app.py"]

Setup Motion Secrets and techniques and Variables 

To securely retailer delicate info like AWS credentials and repository URIs, we have to arrange GitHub Motion Secrets and techniques in our repository. Comply with these steps:

Step1: Open Your GitHub Repository

Navigate to your repository on GitHub.

Step2: Go to Settings

On the prime of your repository web page, find and click on on the “Settings” tab.

Step3: Entry Secrets and techniques and Variables

Within the left sidebar, scroll all the way down to Secrets and techniques and Variables → Choose Actions.

Step4: Create a New Secret

Click on the New repository secret button.

Step5: Add AWS Credentials

• Create a secret with the identify “AWS_ACCESS_KEY_ID” and paste your AWS Entry Key.
• Create one other secret named AWS_SECRET_ACCESS_KEY and paste your AWS Secret Entry Key.
• create a secret named “AWS_REGION” and paste your chosen area (eg., us-east-1).

Step6: Add AWS ECR Repository URI

• Copy your ECR repository URI (e.g., 788614365622.dkr.ecr.ap-southeast-2.amazonaws.com).
• Create a brand new secret named “AWS_ECR_LOGIN_URI” and paste the copied URI.
• Create a brand new secret named “ECR_REPOSITORY_NAME” and paste the identify of the ECR repository e.g., machinepredictive).

AWS EC2 

Now let’s perceive tips on how to create an occasion with AWS EC2. Comply with these steps.

• Navigate to the EC2 service within the AWS Administration Console and click on Launch an occasion.
• Title your occasion (e.g., machinepredictive) and choose Ubuntu as your working system.
• Select the occasion sort as t2.massive.
• Choose your default key pair.
• Beneath community safety, select the default VPC and configure the safety teams as wanted.
• Lastly, click on Launch Occasion.

AWS EC2 CLI

After creating the occasion named machinepredictive, choose its Occasion ID and click on Join. Then, beneath EC2 Occasion Join, click on Join once more. This may open an AWS CLI interface the place you’ll be able to run the mandatory instructions.

Now, enter these instructions within the CLI one after the other to arrange Docker in your EC2 occasion:

sudo apt-get replace -y
sudo apt-get improve
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu
newgrp docker

Maintain your CLI session open. Subsequent, navigate to your GitHub repository, click on on Settings, then go to the Actions part, and beneath Runners, click on New self-hosted runner. Select the Linux runner picture. Now, run the next instructions one after the other in your CLI to obtain and configure your self-hosted runner:

# Create a folder and navigate into it
mkdir actions-runner && cd actions-runner

# Obtain the newest runner package deal
curl -o actions-runner-linux-x64-2.322.0.tar.gz -L https://github.com/actions/runner/releases/obtain/v2.322.0/actions-runner-linux-x64-2.322.0.tar.gz

# Optionally available: Validate the hash
echo "b13b784808359f31bc79b08a191f5f83757852957dd8fe3dbfcc38202ccf5768  actions-runner-linux-x64-2.322.0.tar.gz" | shasum -a 256 -c

# Extract the installer
tar xzf ./actions-runner-linux-x64-2.322.0.tar.gz

# Configure the runner
./config.sh --url https://github.com/karthikponna/Predictive_Maintenance_MLOps --token "Paste your token right here"

# Run the runner
./run.sh

This may obtain, configure, and begin your self-hosted GitHub Actions runner in your EC2 occasion.

After organising the self-hosted GitHub Actions runner, the CLI will immediate you to enter a reputation for the runner. Kind self-hosted and press Enter.

CI/CD with GitHub Actions

You may try the .github/workflows/predominant.yml code file.

Now, let’s dive into what every part of this predominant.yml file does.

• Steady Integration Job: This job runs on an Ubuntu runner to take a look at the code, lint it, and execute unit exams.
• Steady Supply Job: Triggered after CI, this job installs utilities, configures AWS credentials, logs into Amazon ECR, and builds, tags, and pushes your Docker picture to ECR.
• Steady Deployment Job: Operating on a self-hosted runner, this job pulls the newest Docker picture from ECR, runs it as a container to serve customers, and cleans up any earlier photos or containers.

As soon as the Steady Deployment Job completes efficiently, you’ll see an output like this within the AWS CLI.

Open your AWS EC2 occasion by clicking its Occasion ID. Confirm that the occasion state is Operating and find the Public IPv4 DNS. Click on on the “Open deal with” choice to robotically launch your FastAPI software in your browser. Now, let’s dive into FastAPI.

FastAPI

You may try the app.py file. 

On this FastAPI software, I’ve created two predominant routes, one for coaching the mannequin and one other for producing predictions. Let’s discover every route intimately.  

Prepare route

The /prepare endpoint begins the mannequin coaching course of utilizing a predefined dataset, making it straightforward to replace and enhance the mannequin with new information. It’s particularly helpful for retraining the mannequin to enhance accuracy or incorporate new information, guaranteeing the mannequin stays up-to-date and performs optimally.

Predict route

The /predict endpoint accepts a CSV file through POST. This endpoint handles incoming information, leverages the educated mannequin to generate predictions, after which returns the outcomes formatted as JSON. This route is ideal for making use of the mannequin to new datasets, making it environment friendly for large-scale prediction duties.

Within the /predict route, we’ve included a pattern take a look at.csv file; you’ll be able to obtain it right here.

Conclusion

Collectively, we constructed a full production-ready Predictive Upkeep MLOps challenge—from gathering and preprocessing information to coaching, evaluating, and deploying our mannequin utilizing Docker, AWS, and FastAPI. This challenge exhibits how MLOps can bridge the hole between improvement and manufacturing, making it simpler to construct strong, scalable options.

Keep in mind, this information is all about studying and making use of these strategies to your information science tasks. Don’t hesitate to experiment, innovate, and discover new enhancements as you progress. Thanks for sticking with me till the top—continue learning, preserve doing, and continue to grow!

GitHub repo: https://github.com/karthikponna/Predictive_Maintenance_MLOps

Key Takeaways

• We constructed an end-to-end MLOps pipeline that covers information ingestion, mannequin coaching, and deployment.
• Docker, AWS, and FastAPI work collectively seamlessly to maneuver from improvement to manufacturing.
• Dockerizing our ML challenge is vital—it ensures it runs easily in any atmosphere with none dependency complications.
• Steady deployment ensures the mannequin stays environment friendly and up-to-date in real-world purposes.

Regularly Requested Questions

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