New Features for Financial Analytics

As a proud partner of Snowflake, we at Scientific Financial Systems (SFS) were pleased to attend the 2023 Snowflake Summit from June 26- 29 in Las Vegas. At Summit, we joined other data professionals, investment management practitioners, and technology enthusiasts to explore the latest trends and innovations in data management and analytics.

Snowflake did a tremendous job hosting a dynamic and thought provoking conference, and we learned from a variety of sessions on data engineering, data governance, machine learning, artificial intelligence, cloud computing, and advanced analytics. Experts from Snowflake, along with renowned industry professionals, delivered engaging presentations, case studies, and live demonstrations, providing attendees with practical insights and best practices.

The Summit offered ample opportunities for networking and collaboration – we particularly enjoyed meeting live with our Snowflake/SFS partners including Kipi and Sigma Computing, as well as other exciting emerging technology companies building on Snowflake.

Here are some of our take-aways from the Summit:

  • AI was positioned front-and-center as Snowflake highlighted the power of AI techniques to deliver valuable insights from unstructured document datasets. The Snowflake engineering team also demoed a new capability of training ML models within Snowflake using high-performance GPUs. We were particularly impressed by a fascinating presentation by Matt Glickman, VP of Customer Product Strategy, demonstrating the potential of harnessing AI LLMs to formulate financial solutions to control for duration risk within investment portfolios.
  • Snowflake also introduced the launch of the Private Preview of “Snowpark Container Services” that will support the use of embedded containers running within the Snowflake environment to sponsor advanced Snowflake native applications. This new capability will allow us to build and deploy even more powerful and sophisticated financial analytics applications on the Snowflake platform.
  • We were also excited to learn about the launch of the “Snowpark ML Ops” capabilities. At the Summit, Snowflake’s Data Science & ML/AI Field CTO, Caleb Baechtold, walked through a real-time demo highlighting how Snowflake’s new ML “model registry” can be used to manage the production deployment of a wide range of ML modeling techniques. We saw how ML models can now be trained, validated, stored, managed, run, and monitored natively within the Snowflake environment. These new Snowflake features will accelerate the application of advanced, customized ML models for financial modeling and security prediction within SFS’s Quotient™ platform.
  • Native App Framework: The “Native App Framework” was taken to public preview by Snowflake. The framework provides a “native deployment and distribution” model that changes the concept of bringing data to apps. This allows applications such as Quotient to leverage compute capabilities directly where the data already lives, improving efficiency​​.

We are returning from this year’s Snowflake Summit with a renewed sense of excitement to build upon our collaborative development partnership with Snowflake to advance the state-of-the-art in financial analytics.

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