Implements a text recognizer using a hypermodern 2022 DL stack:

• Code quality assurance with pre-commit hooks, GitHub Actions, and pytest
• Real data augmented with data synthesis
• Image encoding via a CNN architecture
• Decoding to text via a Transformer architecture
• Model implementation in PyTorch
• Model training and basic tracking and metrics via PyTorch Lightning and torchmetrics
• Experiment tracking, hyperparameter tuning, and model versioning with W&B
• Model packaging in TorchScript
• Predictor backend containerization via Docker and deployment as a microservice on AWS Lambda
• Basic load testing with Locust
• Pure Python frontend web application in Gradio
• Model monitoring with Gantry