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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.

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.

Discover alternative mobile app distribution platforms as Microsoft phases out AppCenter. This post provides insights into the essential features of a good distribution tool and guides you in selecting the best solution for your app release needs.

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.

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.

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.

Using BigQuery object tables, we analyze unstructured Cloud Storage data with generative AI models and join the results with BigQuery structured data.

As software systems become increasingly complex, observability — the ability to understand a system's internal state based on its external outputs — has become a critical practice for developers and operations teams. Traditional observability approaches struggle to keep up with the scale and complexity of modern applications. As the amount of telemetry data grows, it becomes expensive and complex to navigate. Enter AI and its promise to revolutionize observability.