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

1. 1Configure your ML pipeline using a YAML file
2. 2Build a Snowflake compute environment
3. 3Upload your project to Snowflake using submit_directory
4. 4Test your ML pipeline in Snowflake
5. 5Deploy your pipeline to production
6. 6Monitor your pipeline's performance in Snowflake

Data engineers, app developers, and data scientists can benefit from this framework to streamline ML pipeline deployment to Snowflake

💡 Simplify ML pipeline deployment to Snowflake using a single YAML config and the submit_directory framework

🚀 Deploy production ML pipelines to Snowflake with ease using submit_directory and YAML config! 💡