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.

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.

Businesses increasingly rely on digital technologies to streamline operations, enhance efficiency, and stay competitive. However, with the advantages of digitization come inherent risks and challenges that can disrupt business operations. Recognizing the need for a comprehensive framework to address digital operational resilience, governments worldwide have introduced legislative measures to safeguard businesses and consumers.

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.

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.

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.

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.

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

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.