New Approaches in AI Management: The Gateway API Inference Extension
Discover how the Gateway API Inference Extension addresses challenges with AI models in Kubernetes.
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Discover how the Gateway API Inference Extension addresses challenges with AI models in Kubernetes.
Learn how to effectively manage Sidecar containers in Kubernetes and ensure they start before your main application.
The debate around the use of Artificial Intelligence (AI) in education is polarizing. While some see great opportunities for personalized learning, others fear a dehumanization of teaching and new dependencies. However, if we take an objective look at it, it becomes clear: AI can be a valuable tool — provided we use it responsibly.
Today, every support request influences customer satisfaction, loyalty, and long-term business success. Unstructured processes, lost tickets, and long response times erode trust and efficiency. Many companies struggle with complex, inflexible helpdesk solutions or become entangled in costly dependencies on SaaS providers. This is where Zammad makes a clear difference.
Discover the new features of Gateway API v1.3.0, including percentage-based request mirroring and CORS filters for your Kubernetes applications.
Everyone talks about build pipelines, deployment automation, GitOps, blue/green rollouts, canary releases. Everything is orchestrated, automated, versioned. Sounds stable. But the entire stack relies on a simple point that many teams have ignored for too long: the container and artifact registry.
When running applications in production, you don't need pretty dashboards, but hard data. Performance issues never arise when there's time for debugging. They occur precisely when systems are under load, services are running at peak load, external dependencies start to falter, or network latencies gradually build up.
The question keeps coming up. Development teams deliver features, optimize releases, build clean architectures — yet they still get stuck in infrastructure. Managing Kubernetes clusters, renewing certificates, expanding storage, configuring load balancers, checking backups, monitoring oversight, applying security patches.
Everyone is talking about AI, Large Language Models, inference pipelines, custom LLMs, and co-pilots for all conceivable business processes. What is often forgotten: The real value creation does not occur at the prompt, but in the infrastructure on which the models run.
Most IIoT projects don't fail because of the machines. The sensors work. The controllers provide data. The networks transmit packets. The problem starts one level higher: The data ends up somewhere in the production network, is briefly logged, maybe aggregated, and then? Nothing.
When developing applications for clients today, the next topic quickly arises: How is the software operated in production? Where do staging and production systems run? Who takes over 24/7 operations? What does security look like? Who handles availability, patching, backup, scaling, monitoring?
Processing health data fundamentally differs from traditional corporate IT. It involves not just personal data, but highly sensitive information as defined by Article 9 of the GDPR. Diagnoses, lab results, therapy progress, medication plans, imaging data, and treatment documentation are extremely sensitive. A technical error, security incident, or inadequately secured operational process not only jeopardizes business processes but also the integrity of individuals.
DORA (Digital Operational Resilience Act) is not just another documentation and audit procedure that can be elegantly addressed with a few policies and certificates. DORA delves much deeper into operational operations.
The Online Access Act (OZG) obliges the federal government, states, and municipalities to make administrative services digitally available. On paper, this sounds like software projects. In practice, it's no longer just about the application.
Most discussions about the Cloud Act focus solely on data location. Data center in Frankfurt? ISO-certified? Encrypted? Sounds good.
In modern industrial environments, increasingly complex data streams are emerging at the interface between production and enterprise IT. Production facilities, sensors, and machine controls continuously provide real-time data, which is becoming increasingly important for process optimization, predictive maintenance, quality assurance, and business decisions.
In more and more companies, IT and OT (Operational Technology) are converging. Production facilities, machines, control systems, and sensors deliver massive amounts of real-time data. This information is valuable—but only if it can be quickly, reliably, and securely integrated into the IT infrastructure and further processed. This is where the typical challenges have been arising for years:
Digital sovereignty doesn't start with legal texts or strategy papers – it begins where infrastructure is planned, implemented, and operated. At **ayedo**, this means: **every component, every layer, every interface** is scrutinized. Not out of distrust, but out of responsibility.
The announcement initially sounded straightforward: The **Bundeswehr will build its private cloud infrastructure with the support of Google**. Specifically, BWI GmbH – the IT service provider of the Bundeswehr – has signed a framework agreement with "Google Cloud Public Sector – Germany GmbH" to set up two isolated cloud instances. The term Google itself uses: *Air-Gapped Cloud*.
Digitalization in Germany is advancing, but it requires a solid foundation. This foundation is the IT infrastructure. Data centers form the backbone of our digital economy. Yet, despite growing digital dependencies, Germany lags behind in expanding its data center infrastructure. This is not only an economic risk but also a sovereignty issue.
In the IT industry, traditional sales were long dominated by persistent calls, generic emails, and uninspired PowerPoint slides. But the rules have changed: decision-makers, developers, and tech startups today expect solutions, not products—partnerships, not promises. Welcome to the era of building digital trust.
The cloud was once the epitome of efficiency, scalability, and digital transformation. However, the reality has caught up with many companies: vendor lock-ins, uncontrolled cost increases, compliance risks, and a growing loss of control over sensitive data have led more organizations to consider the option of a cloud exit—and in some cases, to implement it decisively.
For over 10 years, I've been part of the international tech scene with a clear focus: think globally, act locally. I hold a formal degree in computer science and technology, but I've always been self-taught, curious, open to new things, and pragmatic in dealing with the real world.
A senior investigator of the International Criminal Court loses access to his emails – because a US President imposes sanctions. Microsoft complies. Without trial, without justification, without consequences.