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# Automating Python in Snowflake: Notebooks, Stored Procedures & ML Jobs with Tasks

**[Snowflake Community](https://daily.dev/sources/snowflake_comm)** · 5 min read · 0 upvotes · 0 comments

## Summary

A practical guide to automating Python code in Snowflake using three approaches: scheduling Snowflake Notebooks via the UI or Tasks, wrapping Python logic in Stored Procedures triggered by Tasks, and running full Python files via Snowflake ML Jobs on Snowpark Container Services. All three paths are illustrated with a consistent ML inference example using a Random Forest model stored in the Snowflake Model Registry, with guidance on when to use each approach and how to chain tasks into a DAG for multi-step pipelines.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/snowflake/automating-python-in-snowflake-notebooks-stored-procedures-ml-jobs-with-tasks-d0b7ff3ed5a8>

## Questions this post answers

### How do I schedule a Snowflake Notebook to run automatically without writing pipeline code?

Open the notebook in the Snowflake workspace and click the Schedule button in the top-right corner, then set a frequency such as hourly, daily at a specific time, or weekly, choose a warehouse, location, and task name, and save. Snowflake automatically creates a Task behind the scenes so the notebook runs on schedule with no additional setup.

_Data teams scheduling notebook pipelines can track Snowflake workflow tips like this on daily.dev._

### What is a Snowflake ML Job and when should I use one instead of a stored procedure?

An ML Job runs a standalone Python file on Snowpark Container Services compute, purpose-built for ML workloads, giving access to data and models on managed scalable compute within Snowflake's security perimeter. It suits GPU training workloads, containerized applications, or scripts checked into Git that are too large or complex to fit inside a stored procedure, unlike simpler inline Python snippets.

_Engineers weighing compute options for ML pipelines can follow Snowflake ML Jobs coverage on daily.dev._

### How do I trigger a Python stored procedure in Snowflake using a Task without needing a notebook?

Create a Python stored procedure with LANGUAGE PYTHON, a RUNTIME_VERSION, required PACKAGES, and a HANDLER function, then invoke it with CALL my_proc(). Because Tasks can trigger stored procedures natively, no notebook is required, making this approach suited to data engineers productionalizing ML pipelines that must be callable from SQL, applications, or other workflows.

_Developers automating Snowflake pipelines can keep up with stored procedure patterns like this via daily.dev._

## Similar posts on daily.dev

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- [From Workspace to Production: CI/CD for Snowflake’s New Notebooks](https://daily.dev/posts/from-workspace-to-production-ci-cd-for-snowflake-s-new-notebooks-e3octtebp) · Snowflake Community · 0 upvotes · 0 comments
- [Snowflake Builders Blog: Data Engineers, App Developers, AI, & Data Science](https://daily.dev/posts/snowflake-builders-blog-data-engineers-app-developers-ai-data-science-acbmpst5f) · Snowflake Community · 0 upvotes · 0 comments

---

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

[View this post on daily.dev](https://daily.dev/posts/automating-python-in-snowflake-notebooks-stored-procedures-ml-jobs-with-tasks-lq9lcvfaq)

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