Model Training as Code
4 days ago
- #Machine Learning
- #Model Training
- #Software Engineering
- Model training complexity requires specialized stages and teams, and manual coordination doesn't scale. Aleph Alpha developed Savanna, a model factory implementing the training pipeline as code.
- Manual model training incurs hidden costs: human error from manual steps, forgotten learnings due to lack of durable records, and team fragmentation from infrequent hand-offs.
- Model Training as Code (MTaC) in Savanna provides composability (functions with typed inputs/outputs), consensus (version control), and provenance (code comments/commit history) for collaborative pipeline management.
- Savanna uses CI for training triggers, enables small-scale experiments via branch pushes, automates hyperparameter sweeps with caching, and ensures artefact lineage and immutability for reproducibility.
- MTaC has improved iteration speed, allowed easy resumption of large runs, enabled capability-oriented teams, and increased organizational learning, potentially enabling auto-research via LLM agents in the future.