Despite the seemingly nonstop conversation surrounding AI, the data suggests that bringing AI into enterprises is still easier said than done. There’s so much potential and plenty of value to be captured — if you have the right models and tools. Implementing advanced AI today requires a solid data foundation as well as a set of solutions, each demanding its own tools and complex infrastructure.

Much has been said about how generative AI will impact the healthcare and life sciences industries. While generative AI will never replace a human healthcare provider, it is going a long way toward addressing key challenges and bottlenecks in the industry. And the effects are expected to be far-reaching across the sector.

When Snowflake acquired the TruEra AI Observability platform, we committed to keeping TruLens open source. We’re not only keeping that promise; we’re emphasizing it. Our goal remains to support LLM app developers in creating trustworthy generative AI applications. In the weeks since the acquisition, we have already added ecosystem-friendly enhancements including: We plan to continue making enhancements and improvements that benefit the community at large, whether on Snowflake or not.

Thousands of customers have worked with Snowflake to cost-effectively build a secure data foundation as they look to solve a growing variety of business problems with more data. Increasingly customers are looking to expand that powerful foundation to a broader set of data across their enterprise.

In the age of AI, enterprises are increasingly looking to extract value from their data at scale but often find it difficult to establish a scalable data engineering foundation that can process the large amounts of data required to build or improve models. Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems.

The telecom industry is undergoing an unprecedented transformation. Fueled by tech advancements such as 5G, cloud computing, Internet of Things (IoT) and machine learning (ML), telecoms have the opportunity to reshape and streamline operations and make significant improvements in service delivery, customer experience and network optimization.

Snowflake’s mission is to mobilize the entire world’s data, and there are millions of data scientists and developers who don’t have access to full-stack engineering teams. It’s been our endeavor to bring the power of the AI Data Cloud to every individual developer, data scientist and machine learning engineer, so that they can build and share world-class data apps — all by themselves. Streamlit is an open source library that turns Python scripts into shareable web apps.

The journey toward achieving a robust data platform that secures all your data in one place can seem like a daunting one. But at Snowflake, we’re committed to making the first step the easiest — with seamless, cost-effective data ingestion to help bring your workloads into the AI Data Cloud with ease. Snowflake is launching native integrations with some of the most popular databases, including PostgreSQL and MySQL.

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.