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README.md

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 short_description: End-to-End Automated MLOps Framework
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+---
+
+# End-to-End Automated MLOps Framework
+
+This project is a comprehensive, production-ready MLOps platform designed to automate the entire machine learning lifecycle. It provides an enterprise-grade solution for training, versioning, deploying, and monitoring models, complete with automated drift detection, retraining, and A/B testing capabilities.
+
+The entire system is managed through a sophisticated, multi-tabbed dashboard, offering a transparent and interactive view into every stage of the model lifecycle. This framework is built to demonstrate best practices in MLOps and to serve as a robust foundation for real-world machine learning systems.
+
+## Core Features
+
+This platform integrates a full suite of MLOps tools into a single, cohesive system:
+
+* **Automated Training & Hyperparameter Tuning**: Employs a custom PyTorch neural network and leverages `Optuna` for sophisticated, automated hyperparameter optimization to find the best-performing model architecture.
+
+* **Model Registry & Versioning**: A robust model registry, backed by a persistent SQLite database, tracks every model version, its associated metrics, metadata, and artifacts. It supports clear versioning and promotion of models to a production state.
+
+* **Data and Concept Drift Detection**: Integrates powerful libraries like `Evidently` and `Alibi-Detect` to continuously monitor for data drift. It provides detailed reports on drift scores and identifies which features are most affected.
+
+* **Automated Retraining on Drift**: The system can be configured to automatically trigger a model retraining pipeline when significant data drift is detected, ensuring that production models remain accurate and relevant.
+
+* **Live A/B Testing Framework**: A built-in A/B testing manager allows for controlled, live comparison between a champion (production) model and a challenger. It routes traffic, records performance, and uses statistical tests to determine a winner.
+
+* **Comprehensive Performance Monitoring**: Tracks key performance indicators in real-time using `Prometheus` metrics. It monitors prediction latency, throughput, and model accuracy, providing alerts for performance degradation.
+
+* **Detailed Cost Tracking**: An integrated cost tracker estimates the financial impact of the ML system, breaking down costs for training (compute), inference (API calls), and model storage.
+
+* **Automated Model Card Generation**: Generates detailed, shareable model cards that document a model's architecture, performance metrics, training data characteristics, and intended use cases, promoting transparency and responsible AI.
+
+* **One-Click Hugging Face Deployment**: Seamlessly exports any registered model version, along with its model card, to the Hugging Face Hub, making it easy to share and collaborate.
+
+## How It Works
+
+The MLOps Engine orchestrates a continuous, automated loop for managing the model lifecycle:
+
+1.  **Initial Training**: The system begins by training an initial model on a baseline dataset. This process includes hyperparameter optimization with Optuna to find the most effective architecture.
+2.  **Model Registration**: The trained model, its performance metrics, training duration, and metadata are logged in the Model Registry. The best-performing initial model is automatically promoted to "Production."
+3.  **Inference & Monitoring**: The production model serves predictions via the interactive UI. The `PerformanceMonitor` and `CostTracker` log every prediction, tracking latency, confidence, and associated costs.
+4.  **Drift Detection**: On a configurable schedule or by manual trigger, the `DriftDetector` compares incoming data to the reference dataset used for training.
+5.  **Automated Retraining & A/B Testing**:
+    * If significant drift is detected, the system automatically triggers a retraining job on the new data.
+    * The newly trained model becomes a "challenger" and is placed into an A/B test against the current "champion" production model.
+    * The `ABTestManager` splits live traffic between the two models, and the winner is automatically promoted to production after reaching statistical significance.
+6.  **Analysis & Reporting**: At any point, users can generate detailed performance reports, cost breakdowns, and model cards directly from the dashboard.
+
+## Technical Stack
+
+* **Machine Learning & Deep Learning**: PyTorch, Scikit-learn
+* **MLOps & Experiment Tracking**: MLflow, Optuna, Evidently, Alibi-Detect, SHAP
+* **Data Processing & Storage**: Pandas, NumPy, SQLite, Joblib
+* **Monitoring**: Prometheus Client
+* **Deployment & UI**: Gradio, Hugging Face Hub
+* **Core Language**: Python
+
+## How to Use the Demo
+
+The Gradio interface is organized into logical tabs that cover the entire MLOps lifecycle.
+
+1.  **Model Training**: Generate synthetic data and train a new model. Choose whether to run hyperparameter optimization. The training results and performance metrics will be displayed.
+2.  **Model Registry**: View all registered model versions. Select a model and promote it to the production environment.
+3.  **Make Predictions**: Input feature values to get a real-time prediction from the current production model.
+4.  **Drift Detection**: Manually trigger a drift check to compare the current data distribution against the model's training data.
+5.  **A/B Testing**: Start a new A/B test between the production model and a new challenger, check the status of an active test, or complete a test to promote the winner.
+6.  **Performance Monitoring & Cost Tracking**: View dashboards summarizing model performance and operational costs over various time windows.
+7.  **Model Card**: Select any model version and generate a complete documentation card with its metrics and metadata.
+8.  **Settings**: Configure system-level parameters, such as enabling or disabling the automated retraining loop.
+
+## Disclaimer
+
+This project is a comprehensive demonstration of an MLOps framework and operates on synthetically generated data. The models and workflows are designed for educational and illustrative purposes and should be adapted and validated for use in real-world production environments.