Through this quickstart guide, you will explore what's new in Snowpark for Machine Learning. You will set up your Snowflake and Python environments and build an end to end ML workflow from feature engineering to model training and batch inference using Snowpark ML.

What is Snowpark?

Snowpark is the set of libraries and runtimes that securely enable developers to deploy and process Python code in Snowflake.

Client Side Libraries - Snowpark libraries can be installed and downloaded from any client-side notebook or IDE and are used for code development and deployment. Libraries include the Snowpark API for data pipelines and apps and the Snowpark ML API for end to end machine learning.

Elastic Compute Runtimes - Snowpark provides elastic compute runtimes for secure execution of your code in Snowflake. Runtimes include Python, Java, and Scala in virtual warehouses with CPU compute or Snowpark Container Services (public preview) to execute any language of choice with CPU or GPU compute.

Learn more about Snowpark.

What is Snowpark ML?

Snowpark ML includes the Python library and underlying infrastructure for end-to-end ML workflows in Snowflake. With Snowpark ML, data scientists and ML engineers can use familiar Python frameworks for preprocessing, feature engineering, and training models that can be managed entirely in Snowflake without any data movement, silos or governance trade-offs. Snowpark ML has 2 components: Snowpark ML Modeling for model development and Snowpark ML Operations including the Snowpark Model Registry (public preview) for model management and batch inference.


This quickstart will focus on

Using these features, you can build and operationalize a complete ML workflow, taking advantage of Snowflake's scale and security features.

Feature Engineering and Preprocessing: Improve performance and scalability with distributed execution for common scikit-learn preprocessing functions.

Model Training: Accelerate model training for scikit-learn, XGBoost and LightGBM models without the need to manually create stored procedures or user-defined functions (UDFs), and leverage distributed hyperparameter optimization (public preview).


Model Management and Batch Inference: Manage several types of ML models created both within and outside Snowflake and execute batch inference.


By letting you perform these tasks within Snowflake, snowpark-ml provides the following advantages:

The first batch of algorithms provided in Snowpark Python is based on scikit-learn preprocessing transformations from sklearn.preprocessing, as well as estimators that are compatible with those in the scikit-learn, xgboost, and lightgbm libraries.

Learn more about Snowpark ML Modeling API and Snowpark Model Registry.

What you will learn


What You'll Build

Run the following SQL commands in a SQL worksheet to create the warehouse, database and schema.

CREATE OR REPLACE WAREHOUSE ML_HOL_WH; --by default, this creates an XS Standard Warehouse
CREATE OR REPLACE STAGE ML_HOL_ASSETS; --to store model assets

-- create csv format
    SKIP_HEADER = 1 
    TYPE = 'CSV';

-- create external stage with the csv format to stage the diamonds dataset
    URL = 's3://sfquickstarts/intro-to-machine-learning-with-snowpark-ml-for-python/diamonds.csv';


These can also be found in the setup.sql file.

Snowpark for Python and Snowpark ML

  "account"   : "<your_account_identifier_goes_here>",
  "user"      : "<your_username_goes_here>",
  "password"  : "<your_password_goes_here>",
  "role"      : "ACCOUNTADMIN",
  "warehouse" : "ML_HOL_WH",
  "database"  : "ML_HOL_DB",
  "schema"    : "ML_HOL_SCHEMA"

Open the following jupyter notebook and run each of the cells: 1_snowpark_ml_data_ingest.ipynb

Within this notebook, we will clean and ingest the diamonds dataset into a Snowflake table from an external stage. The diamonds dataset has been widely used in data science and machine learning, and we will use it to demonstrate Snowflake's native data science transformers throughout this quickstart.

The overall goal of this ML project is to predict the price of diamonds given different qualitative and quantitative attributes.

Open the following jupyter notebook and run each of the cells: 2_snowpark_ml_feature_transformations.ipynb

In this notebook, we will walk through a few transformations on the diamonds dataset that are included in the Snowpark ML Modeling API. We will also build a preprocessing pipeline to be used in the ML modeling notebook.

Open the following jupyter notebook and run each of the cells: 3_snowpark_ml_model_training_inference.ipynb

In this notebook, we will illustrate how to train an XGBoost model with the diamonds dataset using the Snowpark ML Modeling API. We also show how to execute batch inference through the Snowpark Model Registry.

Congratulations, you have successfully completed this quickstart! Through this quickstart, we were able to showcase Snowpark for Machine Learning through the introduction of Snowpark ML, the Python library and underlying infrastructure for data science and machine learning tasks. Now, you can run data preprocessing, feature engineering, model training, and batch inference in a few lines of code without having to define and deploy stored procedures that package scikit-learn, xgboost, or lightgbm code.

For more information, check out the resources below:

Related Resources