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.

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.

We did a sort of time travel on 20th April at QonfX. If you are not one of the 3000+ people who registered for this event, it is a unique software testing conference that keeps its focus on the Future of Testing. This year was the second edition of QonfX and received even more love than the last time. Feedback like the above filled our social feeds during and post QonfX. We cannot keep a count of the number of times attendees used the words ‘eye-opening’ for the talks given by the speakers.

At the end of every testing project, a test report is usually created to summarize the results. This report provides insights into how the test project was executed if it aligned with the initial plan, and what areas need further optimization. In this article, we’ll explore in-depth what needs to be included in a test report, as well as the key metrics that QA teams need to look at if they want to gauge their testing efficiency.

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.

I have been having quite a bit of fun in this blog series exploring the “sliding doors” (or divergent paths) that organizations can take with their AI and data initiatives. The right door can lead to great value, as I covered in my previous posts on ESG reporting, data products, and responsible AI.

Kernighan & Pike, The Practice of Programming, 1999 Despite constantly changing technologies and the needs of customers, some wisdom seems eternal. Programmers need to test their code. But thorough testing takes time. When we do it well, everything works, and a massive testing effort feels like a waste. However, when we do it badly, our code is often broken, and we wish that we had done better testing. I have some good news for you.

HubSpot, Snowflake and Fivetran marketing executives discuss how a modern data stack enables enterprises to meet customer expectations and drive results.

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.