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Enhancing Data Science and ML in Snowflake with Python, and More

Snowflake is committed to improving how cross-functional teams of data scientists, data engineers, and application developers can collaborate in taking machine learning models from experiments and into production. A big part of this is supporting the most popular language for data science, so we’re going all in on Python. 

This move is the latest in our ongoing effort to help data scientists accelerate their workflow with near-unlimited access to data and data processing power. From the launch of Snowpark, our developer platform, to the rollout of dozens of new data programmability features, we aim to make the lives of data scientists easier. Snowpark empowers developers to build scalable pipelines, machine learning (ML) models, and applications directly in Snowflake using their preferred programming languages. With Snowflake for Python now in public preview, along with a new native integration with Streamlit in development, data teams will be able to build faster, better, and collaborate in new ways

Python joins Snowpark

Python is one of the most popular languages for data science due to its flexibility and rich ecosystem of open source packages. With Snowpark for Python, now in public preview, users can code in a familiar syntax while taking advantage of the scalability, elasticity, security, and compliance benefits developers have come to expect when building with Snowflake. 

As part of Snowpark’s rich programming environment and the Anaconda partnership, teams are able to seamlessly tap into Python’s broad ecosystem of open source packages and libraries all within the Data Cloud . As a result, teams like HyperFinity’s have been able to further streamline their data processing architecture to move projects from development to production more quickly.

Build and deploy Python code directly in Snowflake

Developers have always had the flexibility to use Snowpark from their favorite integrated development environment (IDEs) and development tools of choice. Now, with Snowflake Worksheets for Python, currently in private preview, developers can also build and deploy data pipelines, ML models, and applications directly from Snowsight Snowflake’s user interface, using Python and Snowpark’s DataFrame APIs for Python. Thus, streamlining development with code auto-complete, and the ability to productize custom logic in seconds.  

Expanded support for ML model training

Snowflake’s native support for processing across every step of the ML workflow, including training, helps organizations maintain the highest level of data governance. Now, with Larger Memory Warehouses, in development, users can securely execute memory-intensive operations such as feature engineering and model training on large data sets using popular Python open source libraries available through the Anaconda integration

Bridging the gap between ML-powered insights and business action

One of the biggest challenges can be how the amazing models and insights being created actually get put to use by the rest of the business. This is where the recently acquired Streamlit comes in. Streamilt empowers data scientists and ML engineers to use the language they love, Python, to build delightful tools for their business counterparts.

And with the announcement of Snowflake’s Streamlit integration, currently in development, it will be easy to build, deploy, and share Streamlit apps all within the Data Cloud. Thus, enabling users to build interactive applications in hours, not days or weeks, and securely share, iterate, and collaborate with business teams to increase the impact of development.

Maximizing machine learning adoption with SQL 

Businesses across multiple industries are benefiting from putting ML to use. But many are just scratching the surface. To help these companies extract even more value, Snowflake is making it even easier for more users across an organization to tap into the power of ML-powered predictions. SQL Machine Learning, now in private preview, puts powerful algorithms into the hands of SQL users, starting with time-series forecasting. These can be easily embedded into everyday business intelligence and analytics to improve decision quality and speed.  


Overall, this set of product innovations are unlocking new, more efficient ways to generate and operationalize ML-powered insights. Data scientists, data engineers, and developers can expand their levels of collaboration in taking ML models to production with flexibility to work on the same data using their language of choice, flexibility in infrastructure for different processing needs, and unmatched ease to develop interactive applications that can turn insights into actions.

To learn more about Snowflake’s advances in Data Science, visit Snowflake for Data Science

Snowpark: Code the same, execute faster

Forward Looking Statements

This post contains express and implied forwarding-looking statements, including statements regarding (i) Snowflake’s business strategy, (ii) Snowflake’s products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake’s products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading “Risk Factors” and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements.  As a result, you should not rely on any forwarding-looking statements as predictions of future events. 

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