Systems | Development | Analytics | API | Testing

This article is based on Ballerina Swan Lake Update 4 (2201.4.0).

Now you can create and manage your control plane on-prem or on-cloud, regardless of where your data and compute are. We recently announced extensive new orchestration,scheduling, and compute management capabilities for optimizing control of enterprise AI & ML. Machine learning and DevOps practitioners can now fully utilize GPUs for maximal usage with minimal costs.

The human brain is a prediction machine. It sees patterns, then makes predictions from previous experiences. This part of human intelligence has been critical to our survival. For example, many years ago, a forager might have eaten a particular berry, gotten sick, and thus learned the clues that indicate that a berry is poisonous. This would happen automatically—we’d get nauseous when seeing the berry again, which would make us steer clear.

The rise of AI and LLMs in our world is revolutionizing the applications we’re building and the customer experiences we’re delivering. This is one of the pivotal moments in our industry where we cross over an intersection in our technology evolution to enter a new journey with a paradigm shift. Past intersections were the rise of mobile, the rise of the cloud, and the rise of microservices, among others. Some people may even say that AI is as revolutionary as the birth of the internet.

Across the industry, organizations are attempting to find ways to implement generative AI in their business and operations. But doing so requires significant engineering, quality data and overcoming risks. In this blog post, we show all the elements and practices you need to to take to productize LLMs and generative AI. You can watch the full talk this blog post is based on, which took place at ODSC West 2023, here.

Integrating Artificial Intelligence (AI) in software testing is a major advancement in software development, enhancing efficiency and accuracy while handling complex scenarios. This technological leap introduces significant ethical challenges, such as concerns over data misuse and the need for algorithmic transparency. Understanding and addressing these issues is crucial for fostering responsible innovation in AI.

Generative AI was undoubtedly the most important data moment of 2023, and created a level of excitement for our industry that will certainly continue to be felt in 2024. As we welcome the new year, I am thrilled to share that Qlik is hitting the ground running on that front: today we are announcing the acquisition of groundbreaking technology from Kyndi, an innovator in natural language processing, search, and generative AI.

Generative AI is a powerful tool for rapidly creating and iterating on content of all kinds.

The sheer breadth of data that telecommunications providers collect day-to-day is a huge advantage for the industry. Yet, many providers have been slower to adapt to a data-driven, hyperconnected world even as their services — including streaming, mobile payments and applications such as video conferencing — have driven innovation in nearly every other industry. The speed with which generative AI will change how we work, live, communicate and entertain ourselves is nearly unfathomable.

If 2023 was the year we woke up to how generative AI would change our world, 2024 is the year we realize the change. The real-time AI-driven enterprise may not be pixel-perfect yet, but we’re well on the way. Gen AI has a knock-on effect on all the trends and challenges we will see in 2024. Here’s our take.