This blog series covers how to run, train, fine-tune, and deploy large language models securely inside your Snowflake Account with Snowpark Container Services This year there has been a surge of progress in the world of open source large language models (LLMs). This world of free and open source LLMs took yet another major step forward just this week with Meta’s release of Llama v2.
In the age of big data, where humans generate 2.5 quintillion bytes of data every single day, organizations like yours have the potential to harness more powerful analytics than ever before. But gathering, organizing, and sorting data still proves a challenge. Put simply, there's too much information and not enough context. The most popular commercial data warehouse solutions like Amazon Redshift say they deliver structured, usable data for businesses. But is this true?
Product analytics, as a pivotal component in the modern digital business ecosystem, empowers organizations with data-driven insights to make informed decisions and craft superior user experiences. Particularly, A/B testing and conversion rate optimization (CRO) are critical techniques for fine-tuning mobile applications. This article delves into the technical aspects of implementing and analyzing these strategies, specifically within a mobile context.
Ozone is an Apache Software Foundation project to build a distributed storage platform that caters to the demanding performance needs of analytical workloads, content distribution, and object storage use cases. The Ozone Manager is a critical component of Ozone. It is a replicated, highly-available service that is responsible for managing the metadata for all objects stored in Ozone. As Ozone scales to exabytes of data, it is important to ensure that Ozone Manager can perform at scale.