ml-workflows

What problem does it solve?

ML projects often require stitching together data pipelines, model training loops, experiment tracking, and deployment steps, leading to complex configurations and drift. This guide provides a structured, reusable approach to streamline these workflows from data ingestion to production deployment, reducing duplication and increasing reproducibility.

Core Features & Use Cases

• Data loading, validation, and preprocessing patterns for common ML scenarios.
• End-to-end model development guidance: training loops, evaluation, and reproducibility defaults.
• Experiment tracking and versioning with popular tools (MLflow, Weights & Biases, DVC) for traceability.
• Deployment patterns and infrastructure guidance (saved models, ONNX, serving with FastAPI/TorchServe).

Quick Start

Start by opening this guide and following the data-to-deployment checklist to initialize a reproducible ML project.