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# The Snowflake ML Framework That Ships Itself — Production ML with submit_directory

**[Snowflake Community](https://daily.dev/sources/snowflake_comm)** · 17 min read · 0 upvotes · 0 comments

## Summary

A guide to structuring production ML pipelines on Snowflake using the `submit_directory` API, which ships an entire Python project directory to a compute pool rather than a single function. The framework uses a three-layer architecture (pipelines/source/config), a centralized YAML config file, six independently runnable pipeline stages (feature engineering, training, promotion, inference, scheduling, monitoring), and a CLI with range-based execution for CI/CD integration. Dev tooling including black, ruff, isort, and pre-commit hooks is included to make the codebase team-ready. The post is part 4 of a series evolving from notebook prototypes to a production-grade, multi-engineer ML framework on Snowflake.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/snowflake/the-snowflake-ml-framework-that-ships-itself-production-ml-with-submit-directory-5668e908ba9a>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#snowflake](https://daily.dev/tags/snowflake), [#mlops](https://daily.dev/tags/mlops), [#xgboost](https://daily.dev/tags/xgboost)

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