Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has been facilitating container orchestration for around a decade for both stateful and stateless application workloads. With the recent rise of AI and the advent of tools like Kubeflow and Argo Workflows, Kubernetes is also becoming a first-class citizen when it comes to running AI workloads. When you are training a model on K8s, you may be tweaking many parameters and have to test each of them one by one.

Artificial Intelligence (AI) is reshaping industries worldwide, and Sub-Saharan Africa is no exception. Across the region, governments, businesses, and start-ups are recognising the potential of AI to drive economic growth, improve efficiencies, and enhance decision-making. Yet, as AI adoption accelerates, so does the demand for robust digital infrastructure, including high-performance computing, data centres, and connectivity.

When you think about “AI reliability,” what comes to mind? If you’re like most people, you’re probably thinking of generative AI model accuracy, like responses from ChatGPT, Stable Diffusion, and Sora. While this is certainly important, there’s an even more fundamental type of reliability: the reliability of the infrastructure that your AI models and applications are running on. AI infrastructure is complex, distributed, and automated, making it highly susceptible to failure.

Mobile devs know the struggle. Small regressions can cause big issues in production, and fixing them isn't as easy as pushing a quick patch. Unlike a web app, shipping fixes for apps means navigating app store approvals, and often hopping on meetings with customers to debug because mobile issues can be so challenging to recreate. Catching these issues before the 1-star reviews roll in is crucial. Luckily, Sentry just made it easier than ever.