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Platform Engineering

How to Reduce Costs While Continuously Delivering Value

How can organizations reduce costs while still delivering real value to their customers? This is a question I get asked frequently, and one that a client recently brought to me when they wanted a keynote for their solution architects. In this talk, I walk through the key principles and practical techniques for continuously delivering value while cutting unnecessary spending.

Platform Engineering: How Continuous Value Flow Transforms Commerce

What does “continuous value flow through platform engineering” actually mean? In this Zühlke Commerce Talk, I sat down with my colleague Dennis Kolmitz, Engagement Manager at Zühlke responsible for our commerce customers, to discuss exactly that. We explored why platform engineering is becoming essential for commerce organizations that want to innovate faster, reduce friction, and keep their best talent.

Harnessing the Power of Enterprise Architecture and AI for Strategic Advantage

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.

How to Architect for Continuous Delivery

In September 2024, I had the privilege of delivering a keynote at the Roche DevOps Conference in Poland. The topic: how to architect for continuous delivery. This is a subject close to my heart, because after more than two decades of working in software delivery, I keep seeing the same fundamental patterns that separate high-performing organizations from those that struggle.

Developer Experience and Platform Engineering: The Foundation of Modern Software Delivery

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.

AI-Augmented DevOps with Platform Engineering

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.

Unlocking the Power of AI: Deep Dive into MLOps, Machine Learning, and AI Platforms

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.

Harnessing the Power of Enterprise Architecture and AI for Strategic Benefit

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.

MLOps: From ML Prototypes to Production Through Platform-Based Operationalization

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.