MLOps Simplified: Mastering CIFAR-10 on Your MacBook
This comprehensive guide outlines the creation of an end-to-end MLOps pipeline for a machine learning project based on the CIFAR-10 dataset. It discusses in-depth the technological integration of DVC, GCS, GitHub Actions, Docker, FastAPI, PyTorch, MLFlow, ONNX Runtime and strategies for performance optimization and system monitoring. This pipeline ensures scalable and reproducible machine learning deployments, automation of tasks, and continuous improvement.