The following tutorials provide both local and AWS setups to implement best practices in software and machine learning development. Key features include:
- pyproject: Tracks dependencies for streamlined project management.
- Pre-commit hooks: Ensures high-quality code with automated checks.
- Unit tests with pytest: Enables robust testing for data processing, model selection, training, inference, and evaluation, aligning with the FDA's Good Machine Learning Practices (FDA 2021).
- Documentation (docs, CONTRIBUTING, CODE_OF_CONDUCT): Provides guidance for setup and requirements, outlines contribution protocols, and establishes a code of conduct to foster effective collaboration.
- Datasets (datasets): Manages data policies, preparation, and preprocessing needs.
- Source folder (src): Organises APIs, models, and utilities for clarity and scalability.
This structured approach fosters efficient, maintainable, and scalable workflows!
This project leverages multimodal data, combining X-rays and doctors' reports, to predict diseases in unseen X-ray datasets. It is designed for both local execution and integration with AWS services. See further details here.
This tutorial provides a hands-on introduction to AWS services, including setting up, creating resources, configuring architecture, estimating resource costs, and benchmarking results. For more details, see here.