Snowpark ML

Snowpark ML is a set of tools including SDKs and underlying infrastructure to build and deploy machine learning models. With Snowpark ML, you can pre-process data, train, manage and deploy ML models all within Snowflake, using a single SDK, and benefit from Snowflake’s proven performance, scalability, stability and governance at every stage of the Machine Learning workflow.

Key Components of Snowpark ML

The Snowpark ML Python SDK provides a number of APIs to support each stage of an end-to-end Machine Learning development and deployment process, and includes two key components.

Snowpark ML Development

Snowpark ML Development provides a collection of python APIs enabling efficient ML model development directly in Snowflake:

1. Modeling API (snowflake.ml.modeling) for data preprocessing, feature engineering and model training in Snowflake. This includes the snowflake.ml.modeling.preprocessing module for scalable data transformations on large data sets utilizing the compute resources of underlying Snowpark Optimized High Memory Warehouses, and a large collection of ML model development classes based on sklearn, xgboost, and lightgbm.

2. Framework Connectors: Optimized, secure and performant data provisioning for Pytorch and Tensorflow frameworks in their native data loader formats.

3. FileSet API: FileSet provides a Python fsspec-compliant API for materializing data into a Snowflake internal stage from a query or Snowpark Dataframe along with a number of convenience APIs.

Snowflake MLOps

Snowflake MLOps contains suit of tools and objects to make ML development cycle. It complements the Snowpark ML Development API, and provides end to end development to deployment within Snowflake. Currently, the API consists of:

1. Registry: A python API allows secure deployment and management of models in Snowflake, supporting models trained both inside and outside of Snowflake.
2. Feature Store: A fully integrated solution for defining, managing, storing and discovering ML features derived from your data. The Snowflake Feature Store supports automated, incremental refresh from batch and streaming data sources, so that feature pipelines need be defined only once to be continuously updated with new data.
3. Datasets: Dataset provide an immutable, versioned snapshot of your data suitable for ingestion by your machine learning models.

Getting started

Have your Snowflake account ready

If you don't have a Snowflake account yet, you can sign up for a 30-day free trial account.

Installation

Follow the installation instructions in the Snowflake documentation.

Python versions 3.9 to 3.11 are supported. You can use miniconda or anaconda to create a Conda environment (recommended), or virtualenv to create a virtual environment.

Conda channels

The Snowflake Conda Channel contains the official snowpark ML package releases. The recommended approach is to install snowflake-ml-python this conda channel:

conda install \
  -c https://repo.anaconda.com/pkgs/snowflake \
  --override-channels \
  snowflake-ml-python

See the developer guide for installation instructions.

The latest version of the snowpark-ml-python package is also published in a conda channel in this repository. Package versions in this channel may not yet be present in the official Snowflake conda channel.

Install snowflake-ml-python from this channel with the following (being sure to replace <version_specifier> with the desired version, e.g. 1.0.10):

conda install \
  -c https://raw.githubusercontent.com/snowflakedb/snowflake-ml-python/conda/releases/  \
  -c https://repo.anaconda.com/pkgs/snowflake \
  --override-channels \
  snowflake-ml-python==<version_specifier>

Note that until a snowflake-ml-python package version is available in the official Snowflake conda channel, there may be compatibility issues. Server-side functionality that snowflake-ml-python depends on may not yet be released.

Verifying the package

1. Install cosign. This example is using golang installation: installing-cosign-with-go.

2. Download the file from the repository like pypi.

3. Download the signature files from the release tag.

4. Verify signature on projects signed using Jenkins job:

   cosign verify-blob snowflake_ml_python-1.7.0.tar.gz --key snowflake-ml-python-1.7.0.pub --signature resources.linux.snowflake_ml_python-1.7.0.tar.gz.sig
   
   cosign verify-blob snowflake_ml_python-1.7.0.tar.gz --key snowflake-ml-python-1.7.0.pub --signature resources.linux.snowflake_ml_python-1.7.0

NOTE: Version 1.7.0 is used as example here. Please choose the the latest version.