Today Snowflake is launching and open-sourcing with an Apache 2.0 license the Snowflake Arctic embed family of models. Based on the Massive Text Embedding Benchmark (MTEB) Retrieval Leaderboard, the largest Arctic embed model with only 334 million parameters is the only one to surpass average retrieval performance of 55.9, a feat only less practical to deploy models with over 1 billion parameters are able to achieve.

In March, Snowflake announced exciting releases, including advances in AI and ML with new features in Snowflake Cortex, new governance and privacy features in Snowflake Horizon, and broader developer support with the Snowflake CLI. Read on to learn more about everything we announced last month.

Data is the lifeblood of modern businesses, but unlocking its true insights often requires complex SQL queries. These queries can be time-consuming to write and challenging to maintain. At Snowflake, we believe in making the power of data accessible to all. That’s why we prioritize simplicity, governance and quality in everything we build – including our AI-powered tools.

As Snowflake continues to expand our commitment to compliance, we are pleased to announce that we have successfully completed both C5 and TISAX attestations in Germany.

Snowflake and Salesforce are happy to share that bidirectional data sharing between Snowflake, the Data Cloud company and Salesforce Data Cloud is now generally available. In September, we proudly announced that organizations could begin leveraging Salesforce data directly in Snowflake via zero-ETL data sharing to unify their customer and business data, accelerate decision-making and help streamline business processes.

In December 2023, Snowflake announced its acquisition of data clean room technology provider Samooha. Samooha’s intuitive UI and focus on reducing the complexity of sharing data led to it being named one of the most innovative data science companies of 2024 by Fast Company. Now, Samooha’s offering is integrated into Snowflake and launched as Snowflake Data Clean Rooms, a Snowflake Native App on Snowflake Marketplace, generally available to customers in AWS East, AWS West and Azure West.

As organizations seek to drive more value from their data, observability plays a vital role in ensuring the performance, security and reliability of applications and pipelines while helping to reduce costs. At Snowflake, we aim to provide developers and engineers with the best possible observability experience to monitor and manage their Snowflake environment. One of our partners in this area is Observe, which offers a SaaS observability product that is built and operated on the Data Cloud.

Today, enterprises are focused on enhancing decision-making with the power of AI and machine learning (ML). But the complexity of ML models and data science techniques often leaves behind organizations without data scientists or with limited data science resources. And for those organizations with strong data analyst resources, complex ML models and frameworks may seem overwhelming, potentially preventing them from driving faster, higher-quality insights.

Late last month, innovators from across the telecommunications spectrum — and all the industries that rely on connectivity to succeed — gathered at Mobile World Congress Barcelona (MWC), the biggest telecom conference of the year.

Snowflake is committed to helping our customers unlock the power of artificial intelligence (AI) to drive better decisions, improve productivity and reach more customers using all types of data. Large Language Models (LLMs) are a critical component of generative AI applications, and multimodal models are an exciting category that allows users to go beyond text and incorporate images and video into their prompts to get a better understanding of the context and meaning of the data.