Regulations often get a bad rap. You may have heard the old idiom “cut the red tape” which means to circumvent obstacles like regulations or bureaucracy. But in many – if not most )– cases the underlying need for regulations outweighs the burden of compliance.

At Qlik, we're witnessing a thrilling shift in the landscape of data analysis, customer engagement, and decision-making processes, all thanks to the advent of generative AI, especially Large Language Models (LLMs). The potential for transformation across all sectors is enormous, but the journey toward integration can be daunting for many businesses with many leaders wondering where to start in integrating the exciting capabilities of AI into their daily workflows.

Legacy security information and event management (SIEM) solutions, like Splunk, are powerful tools for managing and analyzing machine-generated data. They have become indispensable for organizations worldwide, particularly for security teams. But as much as security operation center (SOC) analysts have come to rely on solutions like Splunk, there is one complaint that comes up for some: Costs can quickly add up.

If you were to stop someone walking down the street and ask them how long artificial intelligence, or AI, has been a hot topic, they might say it’s something that’s emerged mostly in recent years. But AI has been around for a long time, with the term first being coined as long ago as 1955. Generative AI however is a different beast, and one that's largely responsible for moving the topic of AI to the tip of everyone’s tongues – from consumers to enterprises alike.

In today’s data-driven world, developer productivity is essential for organizations to build effective and reliable products, accelerate time to value, and fuel ongoing innovation. To deliver on these goals, developers must have the ability to manipulate and analyze information efficiently. Yet while SQL applications have long served as the gateway to access and manage data, Python has become the language of choice for most data teams, creating a disconnect.

Support for model fine-tuning in BigQuery takes input text (the prompt) and the expected output, and teaches the model to mimic the behavior.