erratico / self-introduction Goto Github PK

class SelfIntroduction:
    def __init__(self):
        self.programming_language = ['Python', 'Golang', 'Rust']
        self.data_engineering = ['Mysql', 'Bigquery', 'MongoDB', 
                                 'Cloud-Storage', 'PostgreSQL']
        self.feature_store = ['VertexAi-Features', 'Pipeline']
        self.data_science = ['Python', 'Scikit-Learn', 'Tensorflow', 
                             'Pytorch', 'Gensim', 'Opencv', 'Librosa',
                             'Gephi', 'TigerGraph']
        self.mlops = ['Seldon-Core', 'VertexAi-Deploy', 'Kubernetes', 
                      'Docker', 'Helm', 'Istio', 'Terraform', 'Kubectl',
                      'Flask', 'Cloud-Build', 'Google-Cloud-Platform']
        self.business = ['PPT', 'Excel', 'Chart-of-numbers', 'Highcharts', 'HTML']
    
    def complete_process(self):
        # 1.Processing Data and Saving Features to feature store
        self.data_engineering_function()
        # 2.Query feature data from feature store
        self.feature_store_function()
        # 3.Use features to train the model
        self.data_science_function()
        # 4.Deployment Model
        self.mlops_function()
        # 5.Business Value Description
        self.business_function()
    
    def data_engineering_function(self):
        '''
        Read the data and process it into model features, 
        and then save it to the feature store.
        exp:
            Data -> pipline -> Feature -> Feature store
        '''
        print(f"1.Data engineering, tool: {self.data_engineering}.")
        
    def feature_store_function(self):
        '''
        It is used to save the feature data of the model,
        which can improve the feature utilization and version management.
        exp:
            1. Feature set version and key -> Feature
            2. Feature key and time stamp -> Feature
            3. Feature description and time stamp -> Feature
        '''
        print(f"2.Feature store, tool: {self.feature_store}.")
        
    def data_science_function(self):
        '''
        It is used to explore the value of data 
        and transform mature results into models.
        Finally, save the model to the model registry.
        exp:
            Feature -> Training model -> model
        '''
        print(f"3.Data science, tool: {self.data_science}.")
        
    def mlops_function(self):
        '''
        It is used for model deployment (model API), monitoring and evaluation. 
        Developing and deployment commercial API.
        exp:
            model -> API -> model evaluation -> business api -> business evaluation
        '''
        print(f"4.Mlops, tool: {self.mlops}.")
        
    def business_function(self):
        '''
        It is used for the evaluation of business value(make money).
        exp:
            business evaluation -> boss
        '''
        print(f"5.Business, tool: {self.business}.")