Cloud Architecture & Engineering
Designing production-ready and cloud solutions and automated pipelines within the Azure ecosystem to deploy robust backend applications.
Enterprise Data Streaming
Architecting scalable, event-driven streaming pipelines and robust MLOps infrastructure to process real-time enterprise time-series data at scale.
Generative AI & Autonomy
Building LLM-based systems, intelligent RAG pipelines, and self-orchestrating agentic workflows designed for end-to-end enterprise task automation.
Latest Posts
GitLab Management MCP Agent
In our group, we use GitLab not only as our version-control tool but also for planning. Adding and editing issues on the website involves many clicks and a slow interface, so I built an MCP agent to manage issues more efficiently.
Achieving Full Observability with the Grafana Ecosystem
Reaching a coherent, end-to-end observability architecture using the Grafana ecosystem is nontrivial. This post summarises the tooling and architecture I selected to build a fully open-source observability stack while keeping the option to move to Grafana Cloud.
Data Access Layer Abstraction with Apache Spark
How do you build data processing systems that serve multiple customers with varying storage, compute, and scaling needs without rewriting your core logic each time? I show how Apache Spark can be used as an abstraction layer for data access.
Time Series Forecasting — Continuous Ranked Probability Score (CRPS)
Many forecasting models treat over- and under-predictions the same. In practice the stakes often differ. I walk through parametric probabilistic models and introduce CRPS — a loss function designed for training them.
Time Series Forecasting — Quantile Forecasting — Quantile Loss
Traditional forecasting treats over- and under-predictions equally, but real-world costs can differ significantly. Quantile forecasting addresses this by estimating confidence intervals, offering a nuanced view of uncertainty.