Workflow_Orchestration.md

graph LR
    Main_Application_Entry["Main Application Entry"]
    Core_ML_Pipeline_Orchestrator["Core ML Pipeline Orchestrator"]
    Market_Workflow_Orchestrator["Market Workflow Orchestrator"]
    Sport_Workflow_Orchestrator["Sport Workflow Orchestrator"]
    Training_Pipeline["Training Pipeline"]
    Prediction_Pipeline["Prediction Pipeline"]
    Main_Application_Entry -- "initiates" --> Core_ML_Pipeline_Orchestrator
    Core_ML_Pipeline_Orchestrator -- "orchestrates" --> Training_Pipeline
    Core_ML_Pipeline_Orchestrator -- "orchestrates" --> Prediction_Pipeline
    Market_Workflow_Orchestrator -- "configures and initiates" --> Core_ML_Pipeline_Orchestrator
    Sport_Workflow_Orchestrator -- "configures and initiates" --> Core_ML_Pipeline_Orchestrator

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Details

The AlphaPy application's core architecture is centered around a flexible machine learning pipeline. The Main Application Entry serves as the initial launch point, directing control to the Core ML Pipeline Orchestrator. This orchestrator dynamically selects between the Training Pipeline and Prediction Pipeline based on the application's mode. Specialized Market Workflow Orchestrator and Sport Workflow Orchestrator components handle domain-specific data preparation and configuration, subsequently invoking the Core ML Pipeline Orchestrator to execute the appropriate ML phase. This design promotes modularity, allowing for distinct data handling and configuration while centralizing the core ML logic.

Main Application Entry

The primary entry point for the entire AlphaPy application. It is responsible for initiating the overall machine learning workflow, potentially delegating to more specific orchestrators based on user input or configuration.

Related Classes/Methods:

• alphapy.__main__.main:446-514

Core ML Pipeline Orchestrator

Manages the high-level sequence of generic machine learning pipeline phases, specifically handling the training and prediction stages. It acts as a central coordinator for the core ML process, independent of the specific domain (market or sport).

Related Classes/Methods:

• alphapy.__main__.main_pipeline:413-439

Market Workflow Orchestrator

Coordinates the end-to-end workflow for market analysis. This includes loading market-specific configurations, preparing market data, and initiating the execution of the market-specific ML pipeline.

Related Classes/Methods:

• alphapy.market_flow.main:340-441

Sport Workflow Orchestrator

Manages the complete workflow for sport analysis. Its responsibilities include loading sport-specific configurations, preparing and enriching sport-related data, and orchestrating the subsequent ML processes for sports.

Related Classes/Methods:

• alphapy.sport_flow.main:624-918

Training Pipeline

Represents the training phase of an ML pipeline, where models are fitted to data. This component encapsulates the logic for model training, including data preparation, feature engineering, model selection, and evaluation.

Related Classes/Methods:

• alphapy.__main__.training_pipeline:95-302

Prediction Pipeline

Represents the prediction phase of an ML pipeline, where trained models are used to make inferences on new data. This component encapsulates the logic for loading models, preparing new data, and generating predictions.

Related Classes/Methods:

• alphapy.__main__.prediction_pipeline:309-406

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