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.

API-database coupling vs. traditional multi-layered architectures: what’s the difference and why does it matter? The main difference between direct API-database coupling and multi-layered architectures is that the former allows the API to interact directly with the database, minimizing latency and complexity, while the latter uses multiple layers to separate concerns.

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.

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.

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.

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.

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.

Network Telemetry, in simple terms, is like having a conversation with machines or systems located far away. It’s about collecting data from these distant sources to better understand how they’re performing. Think of it as a health check-up, but for machines or systems instead of people. This technology plays a crucial role in a variety of fields.