Snowflake, the Data Cloud company, announced at its Snowday 2023 event new advancements that make it easier for developers to build machine learning (ML) models and full-stack apps in the Data Cloud. Snowflake is enhancing its Python capabilities through Snowpark to boost productivity, increase collaboration, and ultimately speed up end-to-end AI and ML workflows. In addition, with support for containerized workloads and expanded DevOps capabilities, developers can now accelerate development and run apps — all within Snowflake’s secure and fully managed infrastructure.
“The rise of generative AI has made organizations’ most valuable asset, their data, even more indispensable. Snowflake is making it easier for developers to put that data to work so they can build powerful end-to-end machine learning models and full-stack apps natively in the Data Cloud,” said Prasanna Krishnan, Senior Director of Product Management, Snowflake. “With Snowflake Marketplace as the first cross-cloud marketplace for data and apps in the industry, customers can quickly and securely productionize what they’ve built to global end users, unlocking increased monetization, discoverability, and usage.”
Developers Gain Robust and Familiar Functionality for End-to-End Machine Learning
Snowflake is continuing to invest in Snowpark as its secure deployment and processing of non-SQL code, with over 35% of Snowflake customers using Snowpark on a weekly basis (as of September 2023). Developers increasingly look to Snowpark for complex ML model development and deployment, and Snowflake is introducing expanded functionality that makes Snowpark even more accessible and powerful for all Python developers. New advancements include:
- Snowflake Notebooks (private preview): Snowflake Notebooks are a new development interface that offers an interactive, cell-based programming environment for Python and SQL users to explore, process, and experiment with data in Snowpark. Snowflake’s built-in notebooks allow developers to write and execute code, train and deploy models using Snowpark ML, visualize results with Streamlit chart elements, and much more — all within Snowflake’s unified, secure platform.
- Snowpark ML Modeling API (general availability soon): Snowflake’s Snowpark ML Modeling API empowers developers and data scientists to scale out feature engineering and simplify model training for faster and more intuitive model development in Snowflake. Users can implement popular AI and ML frameworks natively on data in Snowflake, without having to create stored procedures.
- Snowpark ML Operations Enhancements: The Snowpark Model Registry (public preview soon) now builds on a native Snowflake model entity and enables the scalable, secure deployment and management of models in Snowflake, including expanded support for deep learning models and open source large language models (LLMs) from Hugging Face. Snowflake is also providing developers with an integrated Snowflake Feature Store (private preview) that creates, stores, manages, and serves ML features for model training and inference.
Endeavor, the global sports and entertainment company that includes the WME Agency, IMG & On Location, UFC, and more, relies on Snowflake’s Snowpark for Python capabilities to build and deploy ML models that create highly personalized experiences and apps for fan engagement.
“Snowpark serves as the driving force behind our end-to-end machine learning development, powering how we centralize and process data across our various entities, and then securely build and train models using that data to create hyper-personalized fan experiences at scale,” said Saad Zaheer, VP of Data Science and Engineering, Endeavor. “With Snowflake as our central data foundation bringing all of this development directly to our enterprise data, we can unlock even more ways to predict and forecast customer behavior to fuel our targeted sales and marketing engines.”
SOURCE: Businesswire