Any (functioning) system is built upon an infrastructure. This infrastructure supports all of the functions and activities of the system. Testing is no exception. With a good test infrastructure, QA teams can speed up their testing efforts and drastically improve efficiency. In this article, we will explore the concept of test infrastructure, and how you can effectively build one.

Generative AI (GenAI) has taken the world by storm, promising to revolutionize industries and transform the way businesses operate. From generating creative content to automating complex tasks, the potential applications of GenAI are vast and exciting. However, implementing GenAI in an enterprise setting comes with its own set of challenges. At Cloudera, we understand the complexities of enterprise GenAI adoption.

Docker has revolutionized application deployment, offering flexibility and efficiency through its containerization technology. As Docker environments evolve and grow, managing resources like containers, images, volumes, and networks becomes increasingly challenging. Docker Cleanup addresses this challenge by providing a systematic approach to identify and remove unused or unnecessary resources. In this blog, Let us explore the essential commands and techniques for cleaning up Docker resources effectively.

In this latest entry of our fascinating interview series that focuses on major players in the global tech arena, we are delighted to present Miguel Jetté, Vice President of Artificial Intelligence at Rev. Join us as we dive into his career development, provide tips for aspiring AI leaders, and discuss the key lessons he has learned along the way.

Our early roadmap in 2024 has been largely focused on improving orchestration and compute infrastructure management capabilities. Last month we released a Resource Allocation Policy Management Control Center with a new, streamlined UI to help teams visualize their compute infrastructure and understand which users have access to what resources.

It is a fairly recent trend for companies to amass GPU firepower to build their own AI computing infrastructure and support the growing number of compute requests. Many recent AI tools now enable data scientists to work on data, run experiments, and train models seamlessly with the ability to submit their jobs and monitor their progress. However, for many organizations with mature supercomputing capabilities, Slurm has been the scheduling tool of choice for managing computing clusters.

For a software engineer, even the basic use of a debugger can save a lot of pain: adding breakpoints (places in the code the program will stop at and expose the current context) is very easy, and navigating from one breakpoint to another isn't difficult either. And with just that, you can say goodbye to a program's many puts and runs. Just add one or more breakpoints and run your program.

For enterprises, commercial use of AI is still in its early stages, and it’s a case of risk and reward, weighing up both and investigating the best way forward. Of course, there’s much to gain from the use of AI. Already, companies are providing better customer service, parsing complex information through natural language inputs, and generally making workflows faster.

Things can get tricky when managing pre-paid, pay-as-you-go billing for monetized APIs. Three mechanisms must be in place for this type of billing to work: first, you need to be able to add credits to an account. Second, you need to be able to burn down those credits. Third, you need to be able to block users from accessing the API once they have run out of credits.