Learn how to use BigQuery DataFrames together with LLMs to generate synthetic data right inside of BigQuery.

Of all of the emerging tech of the last two decades, artificial intelligence (AI) is tipping the hype scale, causing organizations from all industries to rethink their digital transformation initiatives asking where it fits in. In Financial Services, the projected numbers are staggering. According to a recent McKinsey & Co.

Gartner predicts that generative AI will be a workforce partner for 90% of companies globally by 2025. However, Alexander Bant, Gartner Finance’s chief of research, expressed a contrasting view at the Gartner CFO & Finance Executive Conference in Sydney in February 2024. He noted, “The large majority of CFOs continue to be displeased with the performance of digital investments across their organization.”

Multi-tenant applications are crucial for efficiently serving multiple clients from a single shared instance, offering cost savings, scalability, and simplified maintenance. Such applications can allow hospitals and clinics to manage patient records securely, can enable financial institutions to provide personalized banking services, and can help streamline inventory management and customer relationship management across multiple stores.

Black box testing is a testing method where testers evaluate the quality of a system without knowledge of its internal structures. The system is a “black box”: they know what it does, but not how it achieves those results. In this article, we’ll learn more about black box testing in-depth, the common techniques used, and black box testing best practices.

Last year at Summit, we announced the public launch of the Snowflake Performance Index (SPI), an aggregate index for measuring real-world improvements in Snowflake performance experienced by customers over time.

It is estimated that between 80% and 90% of the world’s data is unstructured1, with text files and documents making up a significant portion. Every day, countless text-based documents, like contracts and insurance claims, are stored for safekeeping. Despite containing a wealth of insights, this vast trove of information often remains untapped, as the process of extracting relevant data from these documents is challenging, tedious and time-consuming.

Bringing machine learning (ML) models into production is often hindered by fragmented MLOps processes that are difficult to scale with the underlying data. Many enterprises stitch together a complex mix of various MLOps tools to build an end-to-end ML pipeline. The friction of having to set up and manage separate environments for features and models creates operational complexity that can be costly to maintain and difficult to use.