Today, Snowflake is excited to announce that the Llama 3.1 collection of multilingual large language models (LLMs) are now available in Snowflake Cortex AI, providing enterprises with secure, serverless access to Meta’s most advanced open source model. Snowflake offers the largest context window of any vendor, at 128k, for the Llama 3.1 collection of models.

A robust, modern data platform is the starting point for your organization’s data and analytics vision. At first, you may use your modern data platform as a single source of truth to realize operational gains — but you can realize far greater benefits by adding additional use cases. In this blog, we offer guidance for leveraging Snowflake’s capabilities around data and AI to build apps and unlock innovation.

A potential recipe for disaster proved to be the focus of every data executive’s agenda over the last year. A year ago many data leaders were caught off-guard. Employees embraced new gen AI tools with fervor, driving interest in all AI initiatives. Generative AI had penetrated the enterprise, with gen AI positioned in the Peak Of Inflated Expectation segment on the Gartner Hype Cycle for Artificial IntelligenceI, 20231.

There is a scene in Mission: Impossible – Rogue Nation where Tom Cruise is hanging onto the outside of a jet as it has taken off. And while, yes, he’s going with it, he’s not really on board or in control. Some data executives feel like that. It’s not enough to establish goals — or, the destination in this metaphor. The data strategy must provide a flight plan for making sure you get there — on time, on budget and, of course, safely on board.

There is a growing recognition that insurers can introduce data, analytics and AI into virtually all of the important insurance functions and workflows, including product development, pricing and risk selection, underwriting, claims management, contact center optimization, distribution management, reinsurance, and understanding and shaping customer journeys. Here are some of the exciting ways insurance companies can put data to work.

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.