ai-ml-patterns

What problem does it solve?

This Skill provides best practices and code patterns for building, training, and deploying robust, reproducible machine learning systems.

Core Features & Use Cases

• Reproducible ML: Implement strategies for fixing seeds, versioning data/code, and logging experiments.
• Efficient Training: Utilize optimized DataLoader configurations, learning rate schedulers, and early stopping.
• Data Augmentation: Apply common augmentation pipelines for image data using Albumentations.
• Inference Optimization: Export models to ONNX and perform batch inference efficiently.
• Use Case: When starting a new computer vision project, use these patterns to set up a reproducible training pipeline with data augmentation and efficient data loading.

Quick Start

Apply the set_seed function to ensure your PyTorch model training is reproducible.