In today’s rapidly evolving digital landscape, enterprises must strategically leverage advanced technologies to stay competitive and drive innovation.
Enterprise Architecture Meets AI # This presentation explores the intersection of Enterprise Architecture and Artificial Intelligence, focusing on how organizations can implement and benefit from robust platform strategies.
Is DevOps dead? That claim keeps appearing on the internet, with people arguing that platform engineering is taking over. In this talk, which I gave at the Developer Experience Conference at Roche in Poznan, Poland, I explain why DevOps is absolutely not dead and why platform engineering is the key to making it actually work at scale.
When the CEO or CIO comes to you and says “We need AI in our development process,” the right response is not to start implementing immediately. The right response is to ask: why? In this talk at Conf42 Platform Engineering, I walk through the complete journey from identifying value stream bottlenecks to implementing AI-augmented DevOps on a real platform, including a live demo.
Have you ever wondered how companies build those impressive AI applications and keep them running reliably in production? In this video, I take a deep dive into MLOps, the discipline that makes it possible to continuously develop, deploy, and improve machine learning solutions at enterprise scale.
Enterprise Architecture is a big word that hangs heavy in the air. What does it actually mean for your organisation, and how does AI fit into the picture? In this talk, which I gave at the HSLU (Lucerne University of Applied Sciences and Arts) in association with the Digital Veterans Association, I explore how Enterprise Architecture, platform engineering, and AI come together as a strategic lever for modern organisations.
Many organizations build machine learning prototypes that never make it into production. MLOps provides the practices, culture, and architecture to bridge this gap.
What This Talk Covers # This presentation outlines how MLOps helps organizations move machine learning from isolated prototypes into reliable production systems. It frames MLOps as more than a technical setup: a mindset, culture, and set of practices that unify development and operations across the full ML lifecycle, including experimentation, training, deployment, serving, monitoring, and retraining.
Every year, I go through the latest reports, articles, and industry discussions to compile a comprehensive view of the DevOps trends shaping our industry. For 2024, I mapped every major trend onto the technology adoption lifecycle to give you a clear picture of where each technology, methodology, or capability stands in terms of adoption. This framework helps you understand not just what is trending, but how mature each trend really is.
In this episode of the DevTalk podcast, my colleague Kerry Lothrop and I have a conversation about the state of DevOps. We have known each other for many years at Zühlke, and Kerry wanted to pick my brain on what DevOps really means today, where companies struggle, and where the industry is heading.
GitHub Copilot is reshaping software development by introducing AI-assisted coding into everyday engineering work. But what does this mean in practice?
What This Talk Covers # This presentation explains what GitHub Copilot is, how it works in practice, and how it supports developers through context-aware code suggestions, faster problem-solving, and reduced effort for repetitive tasks.
