Building Intelligent Solutions that Integrate Seamlessly

I help businesses bring AI into real-world systems—through clean architecture, practical machine learning, and thoughtful software design.
From prototype to production, I make complex technologies work where they matter.

Machine Learning meets enterprise software – pragmatic, scalable, integrated

Hi, I’m Oliver, a software developer, architect, and professional in the field for over 23 years – with a focus on .NET, cloud systems, and enterprise integration.

In recent years, I’ve specialized in machine learning and artificial intelligence, with a strong emphasis on practical, real-world solutions that can be integrated into business environments.

I support teams and organizations with:

  • developing and integrating ML models (both traditional and deep learning-based)
  • implementing end-to-end AI projects – from data analysis to production-ready APIs
  • embedding AI components seamlessly into existing .NET and cloud architectures
  • establishing MLOps practices for sustainable model scaling

On this site, you’ll find:

  • information about my services as a consultant, developer, and software architect
  • a growing collection of technical blog posts
  • and digital products like my premium notebook series on machine learning solutions with PyTorch and cloud integration

I look forward to connecting – whether it’s for a project, a consultation, or simply a conversation at eye level.

Learn more

  • It’s Here: My First Premium Notebook Series Is Live!

    Now available: My first premium notebook series: “Anomaly Detection: From Exploration to Deployment”! Follow my journey through real-world anomaly detection using the AITEX fabric dataset. I explored and compared VAEs, PatchCore, and DRAEM and built a complete pipeline, from evaluation to operationalization. 👉 Grab it now in the shop

  • The End of My Profession: A Software Engineer Speaks Out

    I’ve been a software developer for over 20 years. Today, I see my profession slowly disappearing. In this deeply personal article, I reflect on how AI systems like GitHub Copilot are not just assisting developers – but replacing them. What remains for us in a world run by autonomous agents?

  • The Strange Magic Behind LLMs and the Illusion of Thinking

    Why do large language models feel so human to talk to? This post explores the statistical foundations of LLMs, the illusion of thought, and what this might reveal about human cognition, consciousness, and the nature of thinking itself.

  • Introduction to Neural Network Architecture

    Neural network architecture isn’t just about stacking layers. It’s about understanding the hidden structure of information and designing systems that can reveal it. From simple perceptrons to attention-driven transformers, every innovation has been a step toward making machines see, understand, and reason more like us.

  • Understanding L1 Regularization: Why It Leads to Sparse Models

    In machine learning, complex models often risk overfitting, capturing noise instead of patterns. L1 regularization offers an elegant solution: by penalizing the absolute value of model weights, it not only reduces overfitting but also tends to zero out irrelevant features entirely. This leads to simpler, more interpretable models, especially powerful in high-dimensional spaces. In this…