Intro to Machine Learning with Snowpark ML for Python

Overview

This quickstart guide introduces the integration of Snowpark for Machine Learning within Snowflake, demonstrating how to set up both Snowflake and Python environments to build an end-to-end ML workflow. This includes everything from feature engineering to model training and deployment using Snowpark ML, leveraging the power of Snowflake for ML tasks without data movement.

Step-By-Step Guide

Setting up the Snowflake Environment

Run the SQL commands found in setup.sql within a Snowflake SQL worksheet to prepare your Snowflake resources, including the creation of a warehouse, database, schema, and stages for model assets.

Setting up the Python Environment

Environment Setup: Use Miniconda or another Python environment manager to set up an environment with Python 3.9 or higher.
- Download Miniconda from here.
- Create and activate the conda environment:
```
conda env create -f conda_env.yml
conda activate snowpark-ml-hol
```
- Optionally, start a Jupyter notebook server:
```
jupyter notebook &> /tmp/notebook.log &
```
Prepare Your Connection Configuration:
- Update connection.json with your Snowflake account details, including the account name, user name, and password.
Important: For security reasons, do not commit connection.json with your real credentials to Git. Instead, use placeholders or environment variables, and keep your actual credentials secure.

Running the Notebooks

Data Ingestion: 1_snowpark_ml_data_ingest.ipynb guides you through cleaning and ingesting the diamonds dataset into Snowflake.
Feature Transformations: 2_snowpark_ml_feature_transformations.ipynb demonstrates Snowpark ML Modeling API's capabilities for data transformation.
Model Training and Inference: 3_snowpark_ml_model_training_inference.ipynb shows how to train an XGBoost model and execute batch inference through Snowpark Model Registry.

About Snowpark and Snowpark ML

Snowpark offers libraries and runtimes for securely processing Python code in Snowflake, including:

Client-Side Libraries for code development and deployment.
Elastic Compute Runtimes for secure code execution in Snowflake using Python, Java, and Scala.

Snowpark ML focuses on end-to-end ML workflows, enabling:

Feature Engineering and Model Training with popular Python ML frameworks directly in Snowflake.
Model Management and Batch Inference through the Snowpark Model Registry, supporting scalable and secure model management.

Advantages of Using Snowpark ML

No Data Movement: Perform ML tasks within Snowflake, ensuring data security and governance.
Streamlined Model Management: Manage ML models with versioning and role-based access control, suitable for both Python and SQL users.
Scalable and Secure: Leverage Snowflake's computing power for efficient ML workflows.

Getting Started

Begin with setting up your Snowflake environment as per the instructions in setup.sql.
Follow the steps to configure your Python environment securely.
Proceed with the Jupyter notebooks to explore feature engineering, model training, and inference within the secure and scalable framework of Snowpark ML.

Credits

This project builds upon the invaluable hands-on labs and materials provided by Snowflake. For more resources and examples, visit Snowflake Labs on GitHub, and Snowflake Documentation.

OmerGamie/sfguide-intro-to-machine-learning-with-snowpark-ml-for-python-main