Generative AI stands out from other technological breakthroughs due to its remarkable velocity and unprecedented speed. In a matter of mere months since its initial emergence in the limelight, this cutting-edge innovation has already achieved scalability, aiming to attain substantial return on investment. However, it is imperative to effectively harness this formidable technology, ensuring that it can deploy on a large scale and yield outcomes that garner trust from your business stakeholders.
It seems like we are witnessing a new quantum leap of technological advancement, with Generative AI taking the world by storm earlier this year. Generative AI (GenAI) has emerged as a powerful tool that combines artificial intelligence with creativity, empowering machines to generate original content, such as images, music, and even text, that imitates human-like creativity from structured and unstructured data.
Quality assurance is a crucial differentiator in today's software marketplace. Gartner reports, "48% of software engineering leaders say customer or user satisfaction are among the top three objectives they are measured on." (Source: 2022 Gartner Software Engineering Leaders Role Survey). In essence, software quality directly influences customer satisfaction. A cornerstone in ensuring such quality is regression testing.
At Snowflake, we’re helping data scientists, data engineers, and application developers build faster and more efficiently in the Data Cloud. That’s why at our annual user conference, Snowflake Summit 2023, we unveiled new features that further extend data programmability in Snowflake for their language of choice, without having to compromise on governance.
The potential of generative AI and large language models (LLMs) for enterprises is massive. We’ve talked about this opportunity before and at Summit 2023, we announced a number of capabilities that come together to help our customers bring generative AI and LLMs directly to their proprietary data, all delivered through a single, secure platform.
Ancient Greek mathematician Archimedes once said, “Give me a lever long enough and a fulcrum on which to place it, and I shall move the world.” He was right. When you intelligently use the right tools, you can move worlds and accomplish what on the surface seems impossible. Right now, developers are doing the impossible—building applications in record time that truly transform industries, improve business processes, and unlock radical productivity.
Containers have emerged as the modern approach to package code in any language to ensure portability and consistency across environments, especially for sophisticated AI/ML models and full-stack data-intensive apps. These types of modern data products frequently deal with massive amounts of proprietary data.
At ThoughtSpot, we know how important it is for businesses of every size and industry to empower every knowledge worker with personalized, actionable data-driven insights. These insights are your secret sauce to making better business decisions, growing faster, and delivering customer experiences that keep people coming back for more. But how do you scale self-service analytics to business users without completely overwhelming your data teams?
With any new, transformative technology comes a fear of the unknown, as I explored in my first blog of a new series on generative AI. Companies are pulled between acting quickly to avoid being left behind and proceeding with caution to avoid major missteps.
