Using AzureML with Snowflake Snowpark for Machine Learning

In the realm of Data Science and Machine Learning, Python has emerged as the language of choice. Snowflake has long supported Python through its Python Connector, enabling data scientists to interact with data stored in Snowflake from their preferred Python environment. However, this approach often involved writing verbose SQL queries. To address this, Snowflake introduced Snowpark Python, a native Python experience that provides a more expressive and extensible interface to Snowflake, reminiscent of popular libraries like pandas and PySpark. Snowpark brings the power of Python’s extensibility and expressiveness while leveraging all of Snowflake’s core features and the underlying power of SQL. In this blog, we will explore how to work with Snowpark in conjunction with AzureML, and introduce the Snowflake Quickstart for working with Snowpark and AzureML.

Snowpark encompasses client-side APIs and server-side runtimes, extending Snowflake’s capabilities to popular programming languages like Scala, Java, and Python. Snowpark Python, the focus of this blog, empowers data scientists to write Python code in a Spark-like API without the need for verbose SQL queries. This enables a richer set of tools for Snowflake users, facilitating the development of machine learning products and workflows. One of the key advantages of Snowpark for Python is its “Bring Your Own IDE” approach. It seamlessly integrates with Python kernels, allowing users to leverage their preferred development environment, such as Jupyter Notebooks, for data exploration and analysis.

On the other hand, AzureML provides a managed platform for building, training, and deploying machine learning models. It offers an integrated Jupyter authoring notebook instance, eliminating the need to manage servers. AzureML supports distributed training and provides a centralized location for tracking experiments using MLflow, an open-source platform for managing the end-to-end machine learning lifecycle.

Together Snowpark and AzureML can efficiently be used to prepare data, train models and deploy a model for inference all while leveraging the rich features of both platforms. The referenced quickstart will walk you through building the below architecture:

Here’s a high-level overview of the workflow:

1. Load and transform data using Snowpark: Utilize Snowpark’s client-side APIs to load and manipulate data from Snowflake, leveraging its powerful pushdown compute capabilities.
2. Train a machine learning model using AzureML and MLflow: Leverage AzureML’s integrated Jupyter authoring notebook to build and train machine learning models. MLflow provides experiment tracking, code packaging, and model deployment functionalities.
3. Deploy models to Snowflake via User Defined Functions (UDFs): Use Snowpark’s Python UDF support to deploy trained machine learning models directly into Snowflake, enabling scalable and efficient scoring.

This blog and quickstart provide an introduction to Snowpark with AzureML, enabling an end-to-end data science workflow. However, for enterprise applications, data scientists and developers should consider additional aspects:

1. Automating testing, execution, and deployment using GitHub and GitHub Actions.
2. Installation of third-party packages in the Snowpark sandbox if using packages not supported by Anaconda.
3. Explore additional resources like Medium blogs on Snowpark, AzureML, and utilizing Snowflake with Azure. For any further questions or clarifications, reach out to your Snowflake account team.