Financial professionals encounter periods of high activity throughout the year. Whether you serve as a CFO, specialize in taxation, or contribute to the team responsible for closing financial records and generating year-end reports, any time can become crunch time. These intervals demand long hours at the office (or working evenings from your home office) as you diligently tackle the extensive list of tasks that require immediate attention.
With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. But in highly regulated industries where these technologies may be prohibited, the focus is less on off the shelf generative AI, and more on the relationship between their data and how AI can transform their business.
In today’s high-velocity digital arena, businesses are thrust into the whirlwind of global events, rapid technological advancements, and the incessant push for innovation. Yet, amidst the tempest of mergers, digital acceleration, and shifting tech paradigms, charting a confident path towards cloud migration can be daunting.
Snowpark is the set of libraries and runtimes that enables data engineers, data scientists and developers to build data engineering pipelines, ML workflows, and data applications in Python, Java, and Scala. Functions or procedures written by users in these languages are executed inside of Snowpark’s secure sandbox environment, which runs on the warehouse.