v1.2.0 - The Autonomous Pipeline

Release v1.2.0 - The Autonomous Pipeline

This is a major architectural release that elevates the project from a manually-executed pipeline to a truly autonomous, self-healing MLOps system. The core innovation is the implementation of a "closed-loop" mechanism that allows the system to monitor its own performance and automatically trigger corrective actions.

With this upgrade, the project now serves as a complete, functional blueprint for the next generation of resilient, operational AI, demonstrating how MLOps automation can be integrated even in a complex, hybrid quantum-classical framework.

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✨ New Feature: The Closed-Loop Retraining Trigger

The centerpiece of this release is the new self-healing capability, which functions like a thermostat for the AI model:

1. Detection (The Sensor): The monitor_with_qsvm.py script now analyzes the simulated production stream for data drift and calculates a drift_rate.
2. Signaling: If this drift rate exceeds a configurable threshold, the script writes a DRIFT_DETECTED signal to a status file.
3. Automated Trigger (The Wiring): The main CI/CD workflow (main.yml) reads this status file. Upon detecting the signal, it makes a secure API call to the GitHub Actions API.
4. Autonomous Action (The Furnace): This API call triggers a new, completely independent retraining workflow (retrain.yml), which re-executes the entire run_pipeline.py script. This automatically builds a new, updated model adapted to the new data realities.

This entire process is hands-off, creating a resilient system that can adapt to a changing environment without human intervention.

🔧 Foundational MLOps Features (from v1.1.x)

This new autonomous capability is built upon the solid, professional foundation established in previous versions, including:

• A Central "Control Panel" (config.yaml) for all parameters.
• Full MLflow Integration for complete experiment tracking and governance.
• A Modular, Simulator-First Architecture with optional noisy simulation for research.
• A Full Suite of Storytelling Visualizations to prove the value of each stage.
• A Robust CI/CD and Unit Testing Framework to ensure engineering quality.

📈 Future Work

This v1.2 release marks the completion of the core architectural vision. Future work will now focus on applying this powerful, autonomous framework to new research questions, such as:

• Benchmarking the pipeline's performance on real-world, complex datasets.
• Implementing and comparing different Quantum Error Mitigation (QEM) strategies.
• Exploring the trade-offs between retraining frequency and computational cost.

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