Welcome to this three-part blog series where we explore how to transform generative AI (GenAI) into a powerful, real-time solution that drives business success.

In Part I of this blog series, we laid the foundation for understanding how data fuels AI and why having the right data at the right time is essential for success. We explored the basics of AI, including its reliance on structured and unstructured data, and how streaming data can help unlock its full potential.

In a significant stride toward transforming AI infrastructure, ClearML has recently announced a collaboration with AMD. By integrating with AMD’s powerful hardware and open-source ROCm software with ClearML’s silicon-agnostic, end-to-end platform, we’re empowering IT teams and AI builders to innovate with ease across diverse infrastructures and integrate GPUs from multiple vendors.

Integrating OpenAI with BigQuery unlocks powerful possibilities for AI-powered data analysis and advanced insight generation. This guide explores how the OpenAI BigQuery integration enables businesses to transform their data warehouses into intelligent analytics powerhouses.

Predictive analytics is changing how businesses make decisions. Companies can use data, machine learning, and statistical modeling to forecast outcomes with better accuracy. So, how can predictive analytics techniques transform your business? Predictive analytics uses historical data to predict future events. It involves understanding the relationships within your data to predict what's next, impacting industries from retail and healthcare to finance and manufacturing.

Implementing predictive analytics in healthcare empowers healthcare providers to take a data-driven approach to anticipating future events and making informed decisions. It helps healthcare professionals forecast the progression of diseases, plan and optimize resource allocation, and ultimately shift from reactive to proactive care. This approach improves patient health outcomes and overall efficiency.

How the world of generative AI has changed and what new innovations and capabilities mean for you.

In today’s data-driven world, Large-scale error log management is essential for maintaining system functionality. It can be quite difficult to pinpoint the underlying causes of problems and come up with workable solutions when you're working with hundreds of thousands of logs, each of which contains a substantial amount of data. Thankfully, automating this process using fine-tuned AI models—like those from OpenAI—makes it more productive and efficient.

Today we're excited to announce Kong Gateway 3.9! Since unveiling Kong Gateway 3.8 at API Summit 2024 just a few months ago, we’ve been busy making important updates and improvements to Kong Gateway. This release introduces new functionality around LLM support for AI governance and security and threat protection. Keep reading to learn more!

Large Language Models, or LLMs, have become a near-ubiquitous technology in recent years. Promising the ability to generate human-like content with simple and direct prompts, LLMs have been integrated across a diverse array of systems, purposes, and functions, including content generation, image identification and curation, and even heuristics-based performance testing for APIs and other software components.