An end-to-end implementation of a ML System for morphological profiling using self-supervised learning to disantangle the biological signal from experimental noise in biological microscopy images.

• Dataset RXRX1
• AWS S3 for storage
• WandB for Data and model versioning
• Data quality with great_expectations

• Pytorch and PytorchLightning
• AWS EC2 for compute
• WandB for Experiment tracking and Hyperparameter Optimization
• Docker for resource management

• FastAPI
• CircleCI
• AWS Lambda

• Load Data script
• Preprocess images
• Data module
• Data Validation for metadata csv file

• Train resnet baseline
• Setup wandb for experiment tracking and hyperparameter optimization
• evaluation metrics
• ConvNeXt modified for 6 channels
• DenseNet 161
• Pretext task to predict rotation

• linting, syntax, data types.
• full training cycle
• input, outputs shapes
• single batch and epoch
• functionality test: load pretraind and predict with sample examples
• evaluation test

• Setup FastAPI
• CircleCI for CI/CD
• input and output handlers
• Server script

• system health & performance
• rule-based stats(min, max, mean, std)
• data drift(mean pixel value, using projections)
• Setup evaluation store