Day 8/60: Building ML Training Infrastructure (And Hitting Walls)

1. 1Build a data preparation pipeline using train/test splits to prevent data leakage
2. 2Implement an experiment tracker to log metrics, parameters, and artifacts automatically
3. 3Create a model registry for version control of trained models
4. 4Configure checkpointing to save model weights during training
5. 5Apply cross-validation to evaluate model performance

Data scientists and ML engineers can benefit from this infrastructure to ensure reproducibility and collaboration in their projects

💡 Reproducibility is key to successful ML projects, and building a solid infrastructure is crucial for collaboration and deployment

🚀 Build a reproducible ML infrastructure with experiment tracking, model versioning, and checkpointing 📊