ChatGPT has taken the world by storm. Apps powered by large language models (LLMs) have suddenly taken center stage, with OpenAI, Google, Baidu, and Microsoft getting into the act. A spurt of startups and use cases has emerged on the scene. Suddenly, Generative AI, a branch to which these LLMs belong, seems to dominate every conversation, not just in the technology industry but among professionals the world over.

Artificial intelligence (AI) promises outsized gains in business efficiency. And since organizations live or die by their processes, integrating AI into the picture through AI process optimization can help boost ROI with end-to-end process automation and enhancements. This blog will explore five ways AI can be used to improve and optimize process efficiency. But first, let’s explain what AI process optimization means.

Many API developers and companies struggle to find ways to easily set up systems to unlock revenue from their APIs. Some are simple but not customizable while others are complex and require massive engineering effort to actually get it all running. To make things easier, Moesif created the Billing Meters feature which allows for massive customization with a minimal amount of code and engineering effort.

In today’s fast-paced world of software development, it’s crucial to ensure that the products you build meet the needs and expectations of your users. This is where testing comes in, and there are various types of testing that you can perform to achieve this goal. The two important types of testing are API and UI testing.

In software development, integration testing is essential in ensuring that all system components function correctly and work together seamlessly. Critical bugs can go unnoticed without proper integration testing, leading to significant issues in production environments. This blog talks about Integration testing and Types of Integration testing. One example of a bug found in production due to the absence of integration testing is the infamous Equifax data breach in 2017.

We are announcing BigQuery ML inference engine, which allows you to run predictions on a broad range of models hosted across multiple locations.

BigQuery data clean rooms can help organizations create and manage secure environments for privacy-centric data sharing, analysis, and collaboration.

Customizing exceptions is usually not a common concern during software development. But if you catch an error in an observability tool and can't correctly and quickly identify the problem, you may need more information and details about an exception. This article will show you how to customize exceptions in Ruby and mitigate potential future problems due to a lack of error information. Let's dive straight in!

With digital technologies transforming industries, companies adopt Agile and other practices to gain a competitive advantage. DevOps is one such method that has changed software development as we know it. The new-age approach helps accelerate productivity and also enables better collaboration between the development and operation teams. But the biggest benefit of adopting DevOps is faster time-to-market. It has helped around 83% of organizations unlock business value and deliver customer value faster.