Today marks a significant milestone for Qovery and a highly anticipated evolution of our product, especially among Platform Engineers and DevOps professionals. We're thrilled to announce the "Bring Your Own Kubernetes" (BYOK) offer in beta access, a transformative step in our journey towards more flexible and adaptive infrastructure management. Please keep reading to understand why we extend our offer.

In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "neural networks": Let’s go a bit deeper on that. Neural networks are a class of artificial intelligence (AI) and machine learning models inspired by the structure and functioning of the human brain. They are a subset of AI techniques that have gained significant popularity due to their ability to learn and make decisions from data.

Last year, we released Reliability Management, a combination of pre-built reliability tests and scoring to give you a consistent way to define, test, and measure progress toward reliability standards across your organization. Today, we fulfill the next stage of that promise with the release of Custom Reliability Test Suites, Custom Scoring, and Dashboards.

When you think about making your service reliable, what standards and benchmarks are most important? The availability of services? Consistently fast responses? Accurate data? Prioritizing critical and common use cases? These are all important and deserve some focus, but today we’ll put the spotlight on an often overlooked pillar: security. ‍ Cybersecurity incidents can be the most devastating types of incident for your organization.

With automation and CI/CD practices, the entire AI workflow can be run and monitored efficiently, often by a single expert. Still, running AI/ML on GPU instances has its challenges. This tutorial shows you how to meet those challenges using the control and flexibility of CircleCI runners combined with Scaleway, a powerful cloud ecosystem for building, training, and deploying applications at scale.

Collecting data in real-time plays a crucial role in securing, monitoring, and troubleshooting applications. This real-time data, often referred to as ‘runtime data,’ provides unique insights into the application’s behavior, which aren’t available through other collection techniques. But the tricky part is that collecting runtime data comes with challenges.