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The project encountered several roadblocks due to the legacy Python version:
A structured, phased approach was employed for the migration :
Predicts tickets at risk of escalation using advanced ML models.
Analyses customer feedback to prioritise critical issues.
Extracts actionable text from image data through OCR techniques.
Suggests tags for better ticket organisation using historical data.
AI-driven response generation for support agents.
Summarises ticket details for faster comprehension.
Python 3.11.9.
Skilled in ML (PyTorch, TensorFlow), data handling, and testing (pytest, unit test).
Optimized execution using Python 3.11's CPython runtime and JIT interpreter.
Used virtual environments and Docker for isolation, testing, and deployment.
The project is hosted on a scalable cloud-based infrastructure(I am not very aware of the hosting platform this task is handled by the client's side):